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Combating Adversaries with Anti-Adversaries
- Publication Year :
- 2021
-
Abstract
- Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect. In particular, our layer generates an input perturbation in the opposite direction of the adversarial one and feeds the classifier a perturbed version of the input. Our approach is training-free and theoretically supported. We verify the effectiveness of our approach by combining our layer with both nominally and robustly trained models and conduct large-scale experiments from black-box to adaptive attacks on CIFAR10, CIFAR100, and ImageNet. Our layer significantly enhances model robustness while coming at no cost on clean accuracy.<br />Comment: Accepted to AAAI Conference on Artificial Intelligence (AAAI'22)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2103.14347
- Document Type :
- Working Paper