CFA: Class-Wise Calibrated Fair Adversarial Training
Zeming Wei, Yifei Wang, Yiwen Guo, Yisen Wang
Abstract
Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). So far, most existing works focus on en-hancing the overall model robustness, treating each class equally in both the training and testing phases. Although revealing the disparity in robustness among classes, few works try to make adversarial training fair at the class level without sacrificing overall robustness. In this paper, we are the first to theoretically and empirically investigate the preference of different classes for adversarial configu-rations, including perturbation margin, regularization, and weight averaging. Motivated by this, we further propose a Class-wise calibrated Fair Adversarial training frame-work, named CFA, which customizes specific training con-figurations for each class automatically. Experiments on benchmark datasets demonstrate that our proposed CFA can improve both overall robustness and fairness notably over other state-of-the-art methods. Code is available at https://github.com/PKU-ML/CFA.