  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 enhancing 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 configurations, including perturbation margin, regularization,
and weight averaging. Motivated by this, we further propose a
\textbf{C}lass-wise calibrated \textbf{F}air \textbf{A}dversarial training
framework, named CFA, which customizes specific training configurations 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
\url{https://github.com/PKU-ML/CFA}.
