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Combating Adversaries with Anti-Adversaries
Motasem Alfarra · Juan C Perez · Ali Thabet · Adel Bibi · Phil Torr · Bernard Ghanem

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in A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning »

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 anti-adversary layer significantly enhances model robustness while coming at no cost on clean accuracy.

Author Information

Motasem Alfarra (King Abullah University of Science and Technology (KAUST))
Juan C Perez (Universidad de los Andes)
Ali Thabet (KAUST)
Adel Bibi (KAUST)
Phil Torr (Oxford)
Bernard Ghanem (KAUST)

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