Minority Reports Defense: Defending Against Adversarial Patches

Authors: Michael McCoyd, Won Park, Steven Chen, Neil Shah, Ryan Roggenkemper, Minjune Hwang, Jason Xinyu Liu, David Wagner
Published: 2020-04-28

Source: https://arxiv.org/abs/2004.13799

PDF: https://arxiv.org/pdf/2004.13799

AIにより推定されたラベル

防御効果分析 攻撃検出 敵対的攻撃検出

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

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.