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- W3021559611 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." @default.
- W3021559611 created "2020-05-13" @default.
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- W3021559611 date "2020-04-28" @default.
- W3021559611 modified "2023-09-27" @default.
- W3021559611 title "Minority Reports Defense: Defending Against Adversarial Patches" @default.
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