Title: A Closer Look at Dual Batch Normalization and Two-domain Hypothesis In Adversarial Training With Hybrid Samples. Abstract: There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both \textit{adversarial} samples and \textit{clean} samples (termed Hybrid-AT). With the assumption that \textit{adversarial} and \textit{clean} samples are from two different domains, a common practice in prior works is to adopt dual BN, where BN$_{adv}$ and BN$_{clean}$ are used for adversarial and clean branches, respectively. A popular belief for motivating dual BN is that estimating normalization statistics of this mixture distribution is challenging and thus disentangling it for normalization achieves stronger robustness. In contrast to this belief, we reveal that what makes dual BN effective mainly lies in its two sets of affine parameters. Moreover, we demonstrate that the domain gap between adversarial and clean samples is actually not very large, which is counter-intuitive considering the significant influence of adversarial perturbation on the model. Overall, our work sheds new light on understanding the mechanism of dual BN in Hybrid-AT as well as its underlying two-domain hypothesis. 