  Deep neural networks are vulnerable to adversarial attacks, often leading to
erroneous outputs. Adversarial training has been recognized as one of the most
effective methods to counter such attacks. However, existing adversarial
training techniques have predominantly been tested on balanced datasets,
whereas real-world data often exhibit a long-tailed distribution, casting doubt
on the efficacy of these methods in practical scenarios.
  In this paper, we delve into adversarial training under long-tailed
distributions. Through an analysis of the previous work "RoBal", we discover
that utilizing Balanced Softmax Loss alone can achieve performance comparable
to the complete RoBal approach while significantly reducing training overheads.
Additionally, we reveal that, similar to uniform distributions, adversarial
training under long-tailed distributions also suffers from robust overfitting.
To address this, we explore data augmentation as a solution and unexpectedly
discover that, unlike results obtained with balanced data, data augmentation
not only effectively alleviates robust overfitting but also significantly
improves robustness. We further investigate the reasons behind the improvement
of robustness through data augmentation and identify that it is attributable to
the increased diversity of examples. Extensive experiments further corroborate
that data augmentation alone can significantly improve robustness. Finally,
building on these findings, we demonstrate that compared to RoBal, the
combination of BSL and data augmentation leads to a +6.66% improvement in model
robustness under AutoAttack on CIFAR-10-LT. Our code is available at
https://github.com/NISPLab/AT-BSL .
