Towards Fairness-Aware Adversarial Learning
Authors: Yanghao Zhang, Tianle Zhang, Ronghui Mu, Xiaowei Huang, Wenjie Ruan

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.