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arXiv · 2004.13799

Minority Reports Defense: Defending Against Adversarial Patches

Michael McCoyd·Won Park·Steven Chen·Neil Shah·Ryan Roggenkemper·Minjune Hwang·Jason Xinyu Liu·David Wagner
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Abstract

Deep learning image classification is vulnerable to adversarial attack, even if the attacker changes just a small patch of the image. We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch. We demonstrate on CIFAR-10, Fashion MNIST, and MNIST that our defense provides certified security against patch attacks of a certain size.

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Michael McCoyd, Won Park, Steven Chen, Neil Shah, Ryan Roggenkemper, Minjune Hwang, Jason Xinyu Liu, David Wagner. 2020-04-28. Minority Reports Defense: Defending Against Adversarial Patches. https://arxiv.org/abs/2004.13799

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