NDSS- Feature Squeezing Mitigates and Detects Carlini-Wagner Adversarial Examples

Paper Arxiv

Abstract

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

Citations

@article{xu2017feature,
  title={Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples},
  author={Xu, Weilin and Evans, David and Qi, Yanjun},
  journal={arXiv preprint arXiv:1705.10686},
  year={2017}
}

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