Abstract
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously evolving attacks, models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples. To solve this problem, in this paper, we propose to remove adversarial noise by learning generalizable invariant features across attacks which maintain semantic classification information. Specifically, we introduce an adversarial feature learning mechanism to disentangle invariant features from adversarial noise. A normalization term has been proposed in the encoded space of the attack-invariant features to address the bias issue between the seen and unseen types of attacks. Empirical evaluations demonstrate that our method could provide better protection in comparison to previous state-of-the-art approaches, especially against unseen types of attacks and adaptive attacks.
Original language | English |
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Title of host publication | Proceedings of 38th International Conference on Machine Learning (ICML 2021) |
Editors | Marina Meila, Tong Zhang |
Publisher | ML Research Press |
Pages | 12835-12845 |
Number of pages | 11 |
Publication status | Published - 18 Jul 2021 |
Event | 38th International Conference on Machine Learning, ICML 2021 - Virtual Duration: 18 Jul 2021 → 24 Jul 2021 https://icml.cc/virtual/2021/index.html https://icml.cc/Conferences/2021 https://proceedings.mlr.press/v139/ |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 139 |
ISSN (Print) | 2640-3498 |
Conference
Conference | 38th International Conference on Machine Learning, ICML 2021 |
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Period | 18/07/21 → 24/07/21 |
Internet address |