order_of_run.txt

Running with params: source_dataset=mnist, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=mnist, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=fashion, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar10, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=10, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=11, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=12, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=13, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=14, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=15, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=16, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=17, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=18, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=cifar100, source_class=19, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=0, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=1, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=2, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=3, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=4, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=5, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=6, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=7, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=8, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=mnist, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=fashion, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=cifar10, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=cifar100, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=--clean, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18
Running with params: source_dataset=svhn, source_class=9, exposure_dataset=svhn, test_attacks=FGSM PGD-10 PGD-100, batch_size=128, max_epochs=30, clean_flag=, train_attack_step=10, test_step=1, save_step=1, cuda_device=0, loss_threshold=0.0001, model=preactresnet18