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On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
April 10, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Anish Athalye, Nicholas Carlini
arXiv ID
1804.03286
Category
cs.CV: Computer Vision
Cross-listed
cs.CR,
cs.LG,
stat.ML
Citations
173
Venue
arXiv.org
Last Checked
4 months ago
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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