Recently, Adversarial Propagation (AdvProp) improves the standard accuracy of a trained model on clean samples. However, the training speed of AdvProp is much slower than vanilla training. Also, we argue that the use of adversarial samples in AdvProp is too drastic for robust feature learning of clean samples. This paper presents Mixup Propagation (MixProp) to further increase the standard accuracy on clean samples and reduce the training cost of AdvProp. The key idea of MixProp is to use mixup to generate samples for the auxiliary batch normalisation layer. This approach provides a moderate dataset as compared with adversarial samples and saves the time used for adversarial sample generation. The experimental results obtained on several datasets demonstrate the merits and superiority of the proposed method.