  Adversarial training (AT) has shown excellent high performance in defending
against adversarial examples. Recent studies demonstrate that examples are not
equally important to the final robustness of models during AT, that is, the
so-called hard examples that can be attacked easily exhibit more influence than
robust examples on the final robustness. Therefore, guaranteeing the robustness
of hard examples is crucial for improving the final robustness of the model.
However, defining effective heuristics to search for hard examples is still
difficult. In this article, inspired by the information bottleneck (IB)
principle, we uncover that an example with high mutual information of the input
and its associated latent representation is more likely to be attacked. Based
on this observation, we propose a novel and effective adversarial training
method (InfoAT). InfoAT is encouraged to find examples with high mutual
information and exploit them efficiently to improve the final robustness of
models. Experimental results show that InfoAT achieves the best robustness
among different datasets and models in comparison with several state-of-the-art
methods.
