[ICML 2021 Long Talk] Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm.
Mingkang Zhu, Tianlong Chen, Zhangyang Wang
We proposed Homotopy Attack, a homotopy algorithm for sparse adversarial attack that aim to find sparse adversarial perturbations with relatively small magnitudes. The sparsity of the perturbation is regularized by the L_0 norm and the magnitude is constrained by the L_inf norm. The proposed algorithm can achieve much sparser results under the same L_inf constraint when compared to the SOTA.
pytorch
torchvision
scipy 1.2.1
pillow
Download the pretrained Inception-v3 model from https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth and put it into the pretrain folder
python demo_attack.py --img_dir path/to/imgs
--imgdir path to input images
--target_class target class
--dec_factor decreasing factor for lambda per iteration
--val_c constant multiplied with searched intial lambda
--val_w1 weight for post attack stage
--val_w2 weight for post attack stage
--val_gamma threshold of triggering the post attack stage
--max_update max L_0 update per outer iteration
--max_epsilon L_inf constraint
--maxiter max inner iteration to run nmAPG per outer iteration of homotopy
Hint:
For lower --max_epsilon, --val_c should be lower and --max_update should be higher.
For higher --max_epsilon, --val_c should be higher and --max_update should be lower.
Please adjust the parameters accordingly so that the average perturbation per entry is close to the L_inf constraint --max_epsilon, which can lead to sparser results.
For detailed parameter setting, please refer to the Appendix.
@misc{zhu2021sparse,
title={Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm},
author={Mingkang Zhu and Tianlong Chen and Zhangyang Wang},
year={2021},
eprint={2106.06027},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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