Adversarial robustness has attracted extensive studies recently by revealing
the vulnerability and intrinsic characteristics of deep networks. However,
existing works on adversarial robustness mainly focus on balanced datasets,
while real-world data usually exhibits a long-tailed distribution. To push
adversarial robustness towards more realistic scenarios, in this work we
investigate the adversarial vulnerability as well as defense under long-tailed
distributions. In particular, we first reveal the negative impacts induced by
imbalanced data on both recognition performance and adversarial robustness,
uncovering the intrinsic challenges of this problem. We then perform a
systematic study on existing long-tailed recognition methods in conjunction
with the adversarial training framework. Several valuable observations are
obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of
robust accuracy exists under unreliable evaluation, and 3) boundary error
limits the promotion of robustness. Inspired by these observations, we propose
a clean yet effective framework, RoBal, which consists of two dedicated
modules, a scale-invariant classifier and data re-balancing via both margin
engineering at training stage and boundary adjustment during inference.
Extensive experiments demonstrate the superiority of our approach over other
state-of-the-art defense methods. To our best knowledge, we are the first to
tackle adversarial robustness under long-tailed distributions, which we believe
would be a significant step towards real-world robustness. Our code is
available at: this https URL .

本文探讨了长尾分布下的对抗鲁棒性问题，并提出了一个名为 RoBal 的有效算法框架，该框架包括两个模块：一个尺度不变分类器和数据再平衡。