Title: Re-balancing Adversarial Training Over Unbalanced Datasets. Abstract: In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored by previous works. Compared with conventional adversarial training on the balanced dataset, this process falls into the dilemma of generating uneven adversarial examples (AEs) and an unbalanced feature embedding space, causing the resulting model to exhibit low robustness and accuracy on tail data. To combat that, we propose a new adversarial training framework -- Re-balancing Adversarial Training (REAT). This framework consists of two components: (1) a new training strategy inspired by the term effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space. Evaluation results on different datasets and model structures prove that REAT can enhance the model's robustness and preserve the model's clean accuracy.