Hard Adversarial Example Mining for Improving Robust Fairness

Adversarial training (AT) is widely considered the stateof-the-art Various approaches have been proposed to enhance the technique for improving the robustness of deep defense capabilities of DNNs against AEs. Adversarial neural networks (DNNs) against adversarial examples training (AT) has been demonstrated to be one of the (AE). Nevertheless, recent studies have revealed that adversarially most effective strategies [11]. Nevertheless, recent research trained models are prone to unfairness problems, [26, 23] have observed that the adversarially trained models restricting their applicability. In this paper, we empirically usually suffer from a serious unfairness problem, i.e., observe that this limitation may be attributed to serious adversarial there is a noticeable disparity in accuracy between different confidence overfitting, i.e., certain adversarial examples classes, seriously restricting their applicability in real-world with overconfidence. To alleviate this problem, we scenarios. Although some solutions have been proposed, propose HAM, a straightforward yet effective framework via the average robustness fairness score is still low and needs adaptive Hard Adversarial example Mining. HAM concentrates to be urgently addressed. On the other hand, several recent on mining hard adversarial examples while discarding studies [29, 17, 25] have focused on achieving efficient adversarial the easy ones in an adaptive fashion.