  Recent work has demonstrated that neural networks are vulnerable to
adversarial examples. To escape from the predicament, many works try to harden
the model in various ways, in which adversarial training is an effective way
which learns robust feature representation so as to resist adversarial attacks.
Meanwhile, the self-supervised learning aims to learn robust and semantic
embedding from data itself. With these views, we introduce self-supervised
learning to against adversarial examples in this paper. Specifically, the
self-supervised representation coupled with k-Nearest Neighbour is proposed for
classification. To further strengthen the defense ability, self-supervised
adversarial training is proposed, which maximizes the mutual information
between the representations of original examples and the corresponding
adversarial examples. Experimental results show that the self-supervised
representation outperforms its supervised version in respect of robustness and
self-supervised adversarial training can further improve the defense ability
efficiently.
