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Defensive Distillation is Not Robust to Adversarial Examples
July 14, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Nicholas Carlini, David Wagner
arXiv ID
1607.04311
Category
cs.CR: Cryptography & Security
Cross-listed
cs.CV
Citations
345
Venue
arXiv.org
Last Checked
3 months ago
Abstract
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
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