  The information bottleneck (IB) method is a feasible defense solution against
adversarial attacks in deep learning. However, this method suffers from the
spurious correlation, which leads to the limitation of its further improvement
of adversarial robustness. In this paper, we incorporate the causal inference
into the IB framework to alleviate such a problem. Specifically, we divide the
features obtained by the IB method into robust features (content information)
and non-robust features (style information) via the instrumental variables to
estimate the causal effects. With the utilization of such a framework, the
influence of non-robust features could be mitigated to strengthen the
adversarial robustness. We make an analysis of the effectiveness of our
proposed method. The extensive experiments in MNIST, FashionMNIST, and CIFAR-10
show that our method exhibits the considerable robustness against multiple
adversarial attacks. Our code would be released.
