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arXiv:1804.03286cs.CV

On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses

Anish Athalye, Nicholas Carlini

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Abstract

Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%.

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Subjects
cs.CVComputer Vision and Pattern Recognition cs.CRCryptography and Security cs.LGMachine Learning 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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