Corpus ID: 3488815

Towards Deep Learning Models Resistant to Adversarial Attacks

@article{Madry2018TowardsDL,
  title={Towards Deep Learning Models Resistant to Adversarial Attacks},
  author={A. Madry and Aleksandar Makelov and L. Schmidt and D. Tsipras and Adrian Vladu},
  journal={ArXiv},
  year={2018},
  volume={abs/1706.06083}
}

A. Madry, Aleksandar Makelov, +2 authors Adrian Vladu

Published 2018

Computer Science, Mathematics

ArXiv

Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. [...] Key Method Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary.Expand Abstract

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