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arXiv:1802.00420cs.LG

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Anish Athalye, Nicholas Carlini, David Wagner

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Abstract

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers.

arXiv comments
ICML 2018. Source code at https://github.com/anishathalye/obfuscated-gradients
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence cs.CRCryptography and Security

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    • > Letting a computer drive my car is high-risk: If there is a malfunction at 70mph, I could die! Google can't even make GPS mapping work perfectly - it's apparent if you use it for a day. Not to mention that AI vision systems are susceptible to adversarial attacks, which means…

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