Defensive Distillation is Not Robust to Adversarial Examples
Cryptography and Security
2016-07-18 v1 Computer Vision and Pattern Recognition
Abstract
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Cite
@article{arxiv.1607.04311,
title = {Defensive Distillation is Not Robust to Adversarial Examples},
author = {Nicholas Carlini and David Wagner},
journal= {arXiv preprint arXiv:1607.04311},
year = {2016}
}
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