HFAT-Pytorch Implementation

The official implementation of our CVPR 2024 paper "Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training" [Arxiv] [CVPR]

Usage

The experiments are conducted using with a single GeForce RTX 4090 24GB.

• Create a virtual environment in terminal: conda create -n HFAT python=3.8.
• Install necessary packages: pip install -r requirements.txt.
• Download CIFAR-10 dataset and put it in ./datasets.
• Use the .sh file in ./scripts to train.

BibTeX

@InProceedings{Li_2024_CVPR, 
	author = {Li, Qian and Hu, Yuxiao and Dong, Yinpeng and Zhang, Dongxiao and Chen, Yuntian}, 
	title = {Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training}, 
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
	month = {June}, 
	year = {2024}, 
	pages = {24442-24451}
}

@article{li2023focus,
  title={Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training},
  author={Li, Qian and Hu, Yuxiao and Dong, Yinpeng and Zhang, Dongxiao and Chen, Yuntian},
  journal={arXiv preprint arXiv:2312.07067},
  year={2023}
}