Although adversarial training (AT) has proven effective in enhancing the
model's robustness, the recently revealed issue of fairness in robustness has
not been well addressed, i.e. the robust accuracy varies significantly among
different categories. In this paper, instead of uniformly evaluating the
model's average class performance, we delve into the issue of robust fairness,
by considering the worst-case distribution across various classes. We propose a
novel learning paradigm, named Fairness-Aware Adversarial Learning (FAAL). As a
generalization of conventional AT, we re-define the problem of adversarial
training as a min-max-max framework, to ensure both robustness and fairness of
the trained model. Specifically, by taking advantage of distributional robust
optimization, our method aims to find the worst distribution among different
categories, and the solution is guaranteed to obtain the upper bound
performance with high probability. In particular, FAAL can fine-tune an unfair
robust model to be fair within only two epochs, without compromising the
overall clean and robust accuracies. Extensive experiments on various image
datasets validate the superior performance and efficiency of the proposed FAAL
compared to other state-of-the-art methods.

在这篇论文中，我们提出了一种名为公平感知对抗学习（FAAL）的新的学习范式，通过将鲁棒训练问题重新定义为最小 - 最大 - 最大框架，以确保训练模型的鲁棒性和公平性。具体而言，通过利用分布鲁棒优化，我们的方法旨在在不同类别之间找到最差的分布，解决方法保证了高概率的上界性能。FAAL 可以在仅两个迭代周期内将一个不公平的鲁棒模型调整为公平模型，而不牺牲整体准确性和鲁棒性，实验证明了 FAAL 相对于其他先进方法的卓越性能和高效性。