Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness" by Xingjun Ma, Linxi Jiang, Hanxun Huang, Zejia Weng, James Bailey, Yu-Gang Jiang

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Evaluate MD attack

python main.py --defence [Choice from defence models] \
               --attack [MD, MDMT, MDE] \
               --eps 8 --bs 100

• bs as batch size.
• eps as the epsilon.
• Defence models evaluated in the paper are available in the defence folder.
• The following attacks are implemented ['MD', 'MDMT', 'MDE', 'PGD', 'CW', 'PGD-ODI'], Auto Attacks aviliable at this link

Citation

If you use this code in your work, please cite the accompanying paper:

@article{ma2023imbalanced,
  title={Imbalanced gradients: a subtle cause of overestimated adversarial robustness},
  author={Ma, Xingjun and Jiang, Linxi and Huang, Hanxun and Weng, Zejia and Bailey, James and Jiang, Yu-Gang},
  journal={Machine Learning},
  pages={1--26},
  year={2023},
  publisher={Springer}
}

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Part of the code is based on the following repo:

• MART: https://github.com/YisenWang/MART
• TREADES: https://github.com/yaodongyu/TRADES
• RST: https://github.com/yaircarmon/semisup-adv
• AutoAttack: https://github.com/fra31/auto-attack
• ODI-PGD https://github.com/ermongroup/ODS
• Linxi Jiang implementation on MD attack https://github.com/Jack-lx-jiang/MD_attacks