A PyTorch implementation for Adversarial Distributional Training for Robust Deep Learning (NeurIPS 2020)

Usage

Dependencies

Python (3.6.8)
Pytorch (1.3.0)
torchvision (0.4.1)
numpy

Training

We have proposed three different methods for ADT. The command for each training method is specified below.

Training ADT_EXP

python adt_exp.py --model-dir adt-exp

Training ADT_EXP-AM

python adt_expam.py --model-dir adt-expam

Training ADT_IMP-AM

python adt_impam.py --model-dir adt-impam

Evaluation

Evaluation under White-box Attacks

python evaluate_attacks.py --model-path ${MODEL-PATH} --attack-method PGD (or FGSM/MIM/CW)

Evaluation under Transfer-based Black-box Attacks

First change the --white-box-attack argument in evaluate_attacks.py to False. Then run

python evaluate_attacks.py --source-model-path ${SOURCE-MODEL-PATH} --target-model-path ${TARGET-MODEL-PATH} --attack-method PGD (or FGSM/MIM/CW)

Evaluation under SPSA

python spsa.py --model-path ${MODEL-PATH}

Contact and cooperate

If you have any problem about this library or want to contribute to it, please send us an Email at:

Cite

Please cite our paper if you use this code in your own work:

@article{deng2020adversarial,
  title={Adversarial Distributional Training for Robust Deep Learning},
  author={Deng, Zhijie and Dong, Yinpeng and Pang, Tianyu and Su, Hang and Zhu, Jun},
  journal={arXiv preprint arXiv:2002.05999},
  year={2020}
}

thudzj/ADT