This is the Pytorch implementation of the AAAI2021 paper Composite Adversarial Attacks
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torch >= 1.3.0
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torchvision
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advertorch
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tqdm
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pillow
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imagenet_c (Only for unrestricted adversarial attacks)
Collect adv. training models and place it to checkpoints. Then run
python test_attacker.py --batch_size 512 --dataset cifar10 --net_type madry_adv_resnet50 --norm linf
The code is only for test on bird_or_bicycle datasets, also you can adapt it to your own datasets and tasks. For those who want to run on bird_or_bicycle datasets, you must first install bird_or_bicycle:
git clone https://github.com/google/unrestricted-adversarial-examples
pip install -e unrestricted-adversarial-examples/bird-or-bicycle
bird-or-bicycle-download
Then collect unrestricted defense models in Unrestricted Adversarial Examples Challenge, such as TRADESv2, and place it to checkpoints. Finally, run
python test_attacker.py --batch_size 12 --dataset bird_or_bicycle --net_type ResNet50Pre --norm unrestricted
Collect adv. training models and place it to checkpoints. Then run
python test_attacker.py --batch_size 384 --dataset cifar10 --net_type madry_adv_resnet50_l2 --norm l2 --max_epsilon 0.5
For this implementation, the codes are not passing a fully test for L2 Attack due to the much long elapsed time, so there may exists some bugs in L2 attack case
Your can define arbitrary attack policies by giving a list of attacker dict:
For example, if you want to compose MultiTargetedAttack, MultiTargetedAttack and CWLinf_Attack_adaptive_stepsize, just define the list like follows:
[{'attacker': 'MultiTargetedAttack', 'magnitude': 8/255, 'step': 50}, {'attacker': 'MultiTargetedAttack', 'magnitude': 8/255, 'step': 25}, {'attacker': 'CWLinf_Attack_adaptive_stepsize', 'magnitude': 8/255, 'step': 125}]
CAA also supports single attacker without any combination: when you give a list like [{'attacker': 'ODI_Step_stepsize', 'magnitude': 8/255, 'step': 150}], it means you are runing a single ODI-PGD attack.
Now this code supports attackers as follows:
- GradientSignAttack
- PGD_Attack_adaptive_stepsize
- MI_Attack_adaptive_stepsize
- CWLinf_Attack_adaptive_stepsize
- MultiTargetedAttack
- ODI_Cos_stepsize
- ODI_Cyclical_stepsize
- ODI_Step_stepsize
- CWL2Attack
- DDNL2Attack
- GaussianBlurAttack
- GaussianNoiseAttack
- ContrastAttack
- SaturateAttack
- ElasticTransformAttack
- JpegCompressionAttack
- ShotNoiseAttack
- ImpulseNoiseAttack
- DefocusBlurAttack
- GlassBlurAttack
- MotionBlurAttack
- ZoomBlurAttack
- FogAttack
- BrightnessAttack
- PixelateAttack
- SpeckleNoiseAttack
- SpatterAttack
- SPSAAttack
- SpatialAttack
The codes have not been carefully arranged and still been messy now. If you meet any bugs, feel free to report in issues.
@article{brown2018unrestricted,
title={Unrestricted adversarial examples},
author={Brown, Tom B and Carlini, Nicholas and Zhang, Chiyuan and Olsson, Catherine and Christiano, Paul and Goodfellow, Ian},
journal={arXiv preprint arXiv:1809.08352},
year={2018}
}
@article{ding2019advertorch,
title={AdverTorch v0. 1: An adversarial robustness toolbox based on pytorch},
author={Ding, Gavin Weiguang and Wang, Luyu and Jin, Xiaomeng},
journal={arXiv preprint arXiv:1902.07623},
year={2019}
}
@article{rauber2017foolbox,
title={Foolbox: A python toolbox to benchmark the robustness of machine learning models},
author={Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
journal={arXiv preprint arXiv:1707.04131},
year={2017}
}
@article{papernot2016technical,
title={Technical report on the cleverhans v2. 1.0 adversarial examples library},
author={Papernot, Nicolas and Faghri, Fartash and Carlini, Nicholas and Goodfellow, Ian and Feinman, Reuben and Kurakin, Alexey and Xie, Cihang and Sharma, Yash and Brown, Tom and Roy, Aurko and others},
journal={arXiv preprint arXiv:1610.00768},
year={2016}
}
@article{croce2020reliable,
title={Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks},
author={Croce, Francesco and Hein, Matthias},
journal={arXiv preprint arXiv:2003.01690},
year={2020}
}
@article{tashiro2020output,
title={Output Diversified Initialization for Adversarial Attacks},
author={Tashiro, Yusuke and Song, Yang and Ermon, Stefano},
journal={arXiv preprint arXiv:2003.06878},
year={2020}
}
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