Improving Adversarial Robustness via Mutual Information Estimation

Dawei Zhou, Nannan Wang*, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method. Specifically, we first measure the dependence by estimating the mutual information (MI) between outputs and the natural patterns of inputs (called natural MI) and MI between outputs and the adversarial patterns of inputs (called adversarial MI), respectively. We find that adversarial samples usually have larger adversarial MI and smaller natural MI compared with those w.r.t. natural samples. Motivated by this observation, we propose to enhance the adversarial robustness by maximizing the natural MI and minimizing the adversarial MI during the training process. In this way, the target model is expected to pay more attention to the natural pattern that contains objective semantics. Empirical evaluations demonstrate that our method could effectively improve the adversarial accuracy against multiple attacks.

Original languageEnglish
Pages (from-to)27338-27352
Number of pages15
JournalProceedings of Machine Learning Research
Volume162
Publication statusPublished - 17 Jul 2022
Event39th International Conference on Machine Learning, ICML 2022 - Baltimore Convention Center , Baltimore, Maryland, United States
Duration: 17 Jul 202223 Jul 2022
https://icml.cc/Conferences/2022
https://proceedings.mlr.press/v162/

Scopus Subject Areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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