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Certified adversarial robustness with additive noise

Publication ,  Conference
Li, B; Chen, C; Wang, W; Carin, L
Published in: Advances in Neural Information Processing Systems
January 1, 2019

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 intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2019

Volume

32

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, B., Chen, C., Wang, W., & Carin, L. (2019). Certified adversarial robustness with additive noise. In Advances in Neural Information Processing Systems (Vol. 32).
Li, B., C. Chen, W. Wang, and L. Carin. “Certified adversarial robustness with additive noise.” In Advances in Neural Information Processing Systems, Vol. 32, 2019.
Li B, Chen C, Wang W, Carin L. Certified adversarial robustness with additive noise. In: Advances in Neural Information Processing Systems. 2019.
Li, B., et al. “Certified adversarial robustness with additive noise.” Advances in Neural Information Processing Systems, vol. 32, 2019.
Li B, Chen C, Wang W, Carin L. Certified adversarial robustness with additive noise. Advances in Neural Information Processing Systems. 2019.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2019

Volume

32

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology