Before revision:  In this paper, we investigate data augmentation (DA) techniques to address the issue. In contrast to the previous reports in the literature that DA is not effective for regularizing AT, we discover that DA can mitigate overfitting in AT surprisingly well, but they should be chosen deliberately. To utilize the effect of DA further, we propose a simple yet effective auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations  to be similar to each other.
After revision:  In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary 'consistency' regularization loss to optimize, which forces predictive distributions after attacking from two different augmentations to be similar to each other.