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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