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

Detecting AutoAttack Perturbations in the Frequency Domain

Peter Lorenz · Paula Harder · Dominik Straßel · Margret Keuper · Janis Keuper


Recently, adversarial attacks on image classification networks by the AutoAttack (Croce & Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very high attack success rate, most defense approaches are focusing on network hardening and robustness enhancements, like adversarial training. This way, the currently best-reported method can withstand ∼ 66% of adversarial examples on CIFAR10. In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. Instead of hardening a network, we detect adversarial attacks during inference, rejecting manipulated inputs. Based on a rather simple and fast analysis in the frequency domain, we introduce two different detection algorithms. First, a black box detector which only operates on the input images and achieves a detection accuracy of 100% on the AutoAttack CIFAR10 benchmark and 99.3% on ImageNet, for eps = 8/255 in both cases. Second, a whitebox detector using an analysis of CNN featuremaps, leading to a detection rate of also 100% and 98.7% on the same benchmarks.

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