Corpus ID: 204925471

Certified Adversarial Robustness with Additive Noise

@inproceedings{Li2019CertifiedAR,
  title={Certified Adversarial Robustness with Additive Noise},
  author={Bai Li and Changyou Chen and W. Wang and L. Carin},
  booktitle={NeurIPS},
  year={2019}
}

Bai Li, Changyou Chen, +1 author L. Carin

Published in NeurIPS 2019

Computer Science, Mathematics

The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied… CONTINUE READING

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