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On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
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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%.
Forward citations
Cited by 2 Pith papers
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Stateful Detection of Black-Box Adversarial Attacks
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
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Connecting Lyapunov Control Theory to Adversarial Attacks
Connects Lyapunov control theory to a provable defense against weaker adversarial attacks on neural networks.
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