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Defensive Distillation is Not Robust to Adversarial Examples
Published: July 14, 2016
arXiv: 1607.04311v1
Authors
Nicholas Carlini
,
David Wagner
Abstract
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.