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Adversarial Attacks are Reversible via Natural Supervision

ICCV2021

Citation

@InProceedings{Mao_2021_ICCV,
    author    = {Mao, Chengzhi and Chiquier, Mia and Wang, Hao and Yang, Junfeng and Vondrick, Carl},
    title     = {Adversarial Attacks Are Reversible With Natural Supervision},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {661-671}
}

setup

  • Create the environment from the environment.yml file:
  • conda env create -f environment.yml
  • conda activate myenv

CIFAR-10 Experiment

  • Choose the right normalization function in cifar10_defense.py L23-26

  • File cifar10_defense.py is for both training SSL branch and test reversal defense. If you would like to train SSL, do not use --eval_only, and vice versa.

Example Command for running our method:

Semi-SL Carmon et. al.

  • Do not do std, mean normalize, they just use 0-1.

  • Download Carmon et. al.'s model: RobustBackboneClassifier: cifar10_rst_adv.pt.ckpt, Our SSL Model: ssl_model_130.pth

  • Train SSL: CUDA_VISIBLE_DEVICES=0 python cifar10_defense.py --fname unlab_cifar10_srn28-10_carmon --md_path /local/rcs/mcz/2021Spring/RobPretrained/unlabeled-rob/cifar10_rst_adv.pt.ckpt --carmon, if you use our checkponit, you can pass this step.

  • Test: CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python cifar10_defense.py --fname test --md_path /local/rcs/mcz/2021Spring/RobPretrained/unlabeled-rob/cifar10_rst_adv.pt.ckpt --carmon --eval_only --ssl_model_path /local/rcs/mcz/2021Spring/SSRobdata/unlab_cifar10_srn28-10_carmon/March1/ssl_model_130.pth

  • We offer PGD, CW, and BIM attack

  • For AutoAttack, run the following: CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python cifar10_defense_rebAA.py --fname test --md_path /proj/vondrick/mcz/SSRobust/Pretrained_model/unlabeled-rob/cifar10_rst_adv.pt.ckpt --carmon --eval_only --ssl_model_path /proj/vondrick/mcz/SSRobust/Ours/unlab_cifar10_srn28-10_carmon/March1/ssl_model_130.pth --attack-iters 1 --n_views 4

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