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Locally optimal detection of stochastic targeted universal adversarial perturbations

2020-12-08 19:27:39
Amish Goel, Pierre Moulin
arXiv_CV
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2012.04692

PDF

https://arxiv.org/pdf/2012.04692.pdf

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