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arXiv:1902.02322cs.LG

Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?

Nicholas Carlini

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

No.

arXiv comments
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence cs.CRCryptography and Security stat.MLMachine Learning (Statistics)

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  1. InstaHide Disappointingly Wins Bell Labs Prize, 2nd Place 1 comment

    • If you enjoy reading Carlini tear into papers, also check out the following: https://arxiv.org/pdf/2002.08347.pdf https://arxiv.org/pdf/1902.02322.pdf https://arxiv.org/pdf/1804.03286.pdf He definitely doesn't mince words, but it's hard to be mad when he always makes very valid…

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