Locally optimal detection of stochastic targeted universal adversarial perturbations

Amish Goel; Pierre Moulin

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arXiv · 2012.04692

Locally optimal detection of stochastic targeted universal adversarial perturbations

Amish Goel·Pierre Moulin

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Abstract

Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images. In this paper, we derive the locally optimal generalized likelihood ratio test (LO-GLRT) based detector for detecting stochastic targeted universal adversarial perturbations (UAPs) of the classifier inputs. We also describe a supervised training method to learn the detector's parameters, and demonstrate better performance of the detector compared to other detection methods on several popular image classification datasets.

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Amish Goel, Pierre Moulin. 2020-12-08. Locally optimal detection of stochastic targeted universal adversarial perturbations. https://arxiv.org/abs/2012.04692

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