Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

Weilin Xu; David Evans; Yanjun Qi

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arXiv · 1705.10686

Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

Weilin Xu·David Evans·Yanjun Qi

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Abstract

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

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Weilin Xu, David Evans, Yanjun Qi. 2017-05-30. Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples. https://arxiv.org/abs/1705.10686

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