Title: To be robust and to be fair: aligning fairness with robustness. Abstract: Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not yet been properly studied, and the relationship between fairness and accuracy attack still remains unclear. Can we simultaneously improve robustness w.r.t. both fairness and accuracy? To tackle this topic, in this paper, we study the problem of adversarial training and adversarial attacks w.r.t. both metrics. We propose a unified structure for fairness attack which bring together common notions in group fairness, and we theoretically prove the equivalence of fairness attacks against different notions. We show the alignment of fairness and accuracy attack in disadvantaged subgroups, and we theoretically demonstrate that robustness of samples w.r.t. adversarial attack against one metric also benefit from robustness of samples w.r.t. adversarial attack against the other metric. Our work unifies adversarial training and attack w.r.t. fairness and accuracy, where both metrics benefit from robustness of the other metric under adversarial attack. Our study suggests a novel way to incorporate adversarial training with fairness, and experimental results show that our proposed method achieves better performance in terms of robustness w.r.t. both fairness and accuracy.