Hard Adversarial Example Mining for Improving Robust Fairness
Chenhao Lin Xiang Ji Yulong Yang Qian Li Chao Shen Run Wang Liming Fang
Adversarial training (AT) is widely considered the state-of-the-art technique
for improving the robustness of deep neural networks (DNNs) against adversarial
examples (AE). Nevertheless, recent studies have revealed that adversarially
trained models are prone to unfairness problems, restricting their
applicability. In this paper, we empirically observe that this limitation may
be attributed to serious adversarial confidence overfitting, i.e., certain
adversarial examples with overconfidence. To alleviate this problem, we propose
HAM, a straightforward yet effective framework via adaptive Hard Adversarial
example Mining.HAM concentrates on mining hard adversarial examples while
discarding the easy ones in an adaptive fashion. Specifically, HAM identifies
hard AEs in terms of their step sizes needed to cross the decision boundary
when calculating loss value. Besides, an early-dropping mechanism is
incorporated to discard the easy examples at the initial stages of AE
generation, resulting in efficient AT. Extensive experimental results on
CIFAR-10, SVHN, and Imagenette demonstrate that HAM achieves significant
improvement in robust fairness while reducing computational cost compared to
several state-of-the-art adversarial training methods. The code will be made
publicly available.