Papers › Adversarial Attacks are Reversible with Natural Supervision

Adversarial Attacks are Reversible with Natural Supervision

26 Mar 2021ICCV 2021 10arXiv:2103.14222archive 2025-07-28

Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, Carl Vondrick

We find that images contain intrinsic structure that enables the reversal of many adversarial attacks. Attack vectors cause not only image classifiers to fail, but also collaterally disrupt incidental structure in the image. We demonstrate that modifying the attacked image to restore the natural structure will reverse many types of attacks, providing a defense. Experiments demonstrate significantly improved robustness for several state-of-the-art models across the CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. Our results show that our defense is still effective even if the attacker is aware of the defense mechanism. Since our defense is deployed during inference instead of training, it is compatible with pre-trained networks as well as most other defenses. Our results suggest deep networks are vulnerable to adversarial examples partly because their representations do not enforce the natural structure of images.

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