  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: https://github.com/wutong16/Adversarial_Long-Tail .
