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The Ethereal
Minority Reports Defense: Defending Against Adversarial Patches
April 28, 2020 ยท Declared Dead ยท ๐ ACNS Workshops
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Michael McCoyd, Won Park, Steven Chen, Neil Shah, Ryan Roggenkemper, Minjune Hwang, Jason Xinyu Liu, David Wagner
arXiv ID
2004.13799
Category
cs.LG: Machine Learning
Cross-listed
cs.CR,
cs.CV,
stat.ML
Citations
70
Venue
ACNS Workshops
Last Checked
5 months ago
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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