Improving Adversarial Robustness by Learning Shared Information

    •  Yu, X., Smedemark-Margulies, N., Aeron, S., Koike-Akino, T., Moulin, P., Brand, M., Parsons, K., Wang, Y., "Improving Adversarial Robustness by Learning Shared Information", Pattern Recognition, DOI: 10.1016/​j.patcog.2022.109054, Vol. 134, pp. 109054, November 2022.
      BibTeX TR2022-141 PDF
      • @article{Yu2022nov,
      • author = {Yu, Xi and Smedemark-Margulies, Niklas and Aeron, Shuchin and Koike-Akino, Toshiaki and Moulin, Pierre and Brand, Matthew and Parsons, Kieran and Wang, Ye},
      • title = {Improving Adversarial Robustness by Learning Shared Information},
      • journal = {Pattern Recognition},
      • year = 2022,
      • volume = 134,
      • pages = 109054,
      • month = nov,
      • doi = {10.1016/j.patcog.2022.109054},
      • issn = {0031-3203},
      • url = {}
      • }
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  • Research Areas:

    Artificial Intelligence, Machine Learning, Signal Processing


We consider the problem of improving the adversarial robustness of neural networks while retaining natural accuracy. Motivated by the multi-view information bottleneck formalism, we seek to learn a representation that captures the shared information between clean samples and their corresponding adversarial samples while discarding these samples’ view-specific information. We show that this approach leads to a novel multi-objective loss function, and we provide mathematical motivation for its components towards improving the robust vs. natural accuracy tradeoff. We demonstrate enhanced tradeoff compared to current state-of-the- art methods with extensive evaluation on various benchmark image datasets and architectures. Ablation studies indicate that learning shared representations is key to improving performance.


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