Adversarial robustness meets imperfect training set

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The AI algorithms can be adversarially fooled, which urgently requires developing adversarially robust learning algorithms in safety-critical applications. On the other hand, learning AI algorithms generally requires massive and high-quality data, which is costly and sometimes unrealistic; thus, an imperfect (e.g., noisy-label or poisoned) data unavoidably co-exists in the learning phase.In FY2022, we have developed adversarially robust learning algorithms with the support of the JSPS KAKENHI Grant, which can handle imperfect training sets, including noisy labels [1] and complementary labels [2]. Additionally, we have utilized a collaborative scheme with multiple models to further improve adversarial robustness [3]. Our research findings have been published in high-profile machine learning conferences and journals.[1] NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels. J. Zhang*, X. Xu, B. Han, T. Liu, N. Gang, L. Cui, M. Sugiyama. Transactions on Machine Learning Research (TMLR 2022)[2] Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. J. Zhou, J. Zhou, J. Zhang*, T. Liu, G. Niu, B. Han, M. Sugiyama. 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)[3] Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. S. Cui, J. Zhang*, J. Liang, B. Han, M. Sugiyama, C. Zhang. 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)