ST-AT

Code for our ICLR 2023 paper Squeeze Training for Adversarial Robustness.

Environments

• Python 3.6.8
• numpy 1.19.5
• pandas 1.1.5
• Pillow 9.0.0
• scipy 1.5.4
• statsmodels 0.12.2
• torch 1.8.0+cu111
• torchvision 0.9.0+cu111

Datasets

Datasets should be prepared into the st-at/data.

Train

Train a ResNet-18 on CIFAR-10/CIFAR-100/SVHN using our ST:

dataset=${dataset} beta=${beta} epochs=${epochs} lossmode=${lossmode} bash st.sh

where ${dataset} in ["cifar10", "cifar100", "svhn"], ${epochs} in ["120", "80"], and ${lossmode} in ["js","l2","symmkl"].

Train a WRN-28-10 on CIFAR-10 using ST-RST:

bash st_rst.sh

Test

FGSM, PGD and CW for ST trained ResNet-18:

python3 test.py --model-path ${modelpath} --log ${logpath} --dataset ${dataset}

PGD_RST for ST-RST trained WRN-28-10:

python3 test.py --model_path ${modelpath} --attack pgd --output_suffix pgd_rst

AutoAttack

python3 test_aa.py --model-path ${modelpath} --arch ${architecture} --dataset ${dataset} --log ${logpath}

where ${architecture} in ["resnet18", "wrn-28-10"].

Citation

Please cite our work in your publications if it helps your research:

@article{li2023squeeze,
  title={Squeeze Training for Adversarial Robustness},
  author={Li, Qizhang and Guo, Yiwen and Zuo, Wangmeng and Chen, Hao},
  booktitle={ICLR},
  year={2023}
}