Adversarial Attack algorithm RGD

Codes for paper Rethinking PGD Attack: Is Sign Function Necessary? by Junjie Yang, Tianlong Chen, Xuxi Chen, Zhangyang Wang, Yingbin Liang.

How to use our code.

Our adversarial attack algorithm is built on AdverTorch, AutoAttack and RobustBench where we demonstrate the performance improvement of proposed RGD over PGD and Auto-Attack. Please make sure to install above packages and its dependecies before using this code. We have tested this code in Python 3.8 with PyTorch 2.0.1.

Detailed instructions.

We introduce the following hyperparameters to run the code in attack.py.

• --nb_iter: Attack steps of each algorithm.
• --eps: Epsilon ball size, 0.0314=8/255, we should use the value which is smaller than 1.
• --alpha: The step size for each update. For RGD, we should expect to use large alpha, e.g., 100. For PGD, smaller alpha is preferred, e.g., 2/255.
• --update_method: Specify the attack algorihm. "sign_grad" refers the PGD/AutoAttack, "raw_grad" refers the PGD(Raw), "noclip_raw" refers the RGD, "rgd+aa" refer to update with RGD first and switch to AutoAttack.
• --alg: If we train the algorithm in vanilla PGD or AutoAttack contexts.
• --model_name: Imported model from RobustBench.
• --init_method: Zero initial or random initial. Zero initial is preferred for RGD and random initial is preferred for PGD.
• --rgd_iter: In RGD+AA algorithm, the number of steps to update with RGD.

We provide following commands to run the code:

Vanilla PGD with 8/255 epsilon ball:

python attack.py --alpha 0.02 --update_method sign_grad --eps 0.0314

RGD with 8/255 epsilon ball:

python attack.py --alpha 3000 --update_method noclip_raw --init_method zero_init --eps 0.0314

RGD+AA with 8/255 epsilon ball:

python attack.py --alpha 3000 --update_method rgd_aa --alg AA --init_method zero_init --eps 0.0314

Citation

If this repo is useful for your research, please cite our paper:

@article{yang2023rethinking,
  title={Rethinking PGD Attack: Is Sign Function Necessary?},
  author={Yang, Junjie and Chen, Tianlong and Chen, Xuxi and Wang, Zhangyang and Liang, Yingbin},
  journal={arXiv preprint arXiv:2312.01260},
  year={2023}
}