This repository contains resources and code related to adversarial robustness in machine learning models. The primary focus is on techniques and experiments to evaluate and improve the robustness of models against adversarial attacks.
TextAttack.ipynb: A Jupyter notebook demonstrating adversarial attacks and defenses using the TextAttack library. This notebook includes examples of generating adversarial examples, evaluating model robustness, and applying defense strategies.
To get started with this project, you will need to have Python installed along with the necessary dependencies. It is recommended to use a virtual environment.
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Clone the repository:
git clone https://github.com/TongmengXie/adversarial_robustness.git cd adversarial_robustness -
Install required packages:
pip install -r requirements.txt(If a
requirements.txtfile is not present, install dependencies manually as needed, e.g.,textattack.)
Open the TextAttack.ipynb notebook in Jupyter Notebook or JupyterLab to explore the adversarial attack experiments. The notebook contains step-by-step instructions and code cells to run attacks and analyze results.
Contributions are welcome! Please open issues or submit pull requests for improvements, bug fixes, or new features related to adversarial robustness.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or suggestions, please open an issue or contact the repository owner.