  In this paper, we propose a novel method, IB-RAR, which uses Information
Bottleneck (IB) to strengthen adversarial robustness for both adversarial
training and non-adversarial-trained methods. We first use the IB theory to
build regularizers as learning objectives in the loss function. Then, we filter
out unnecessary features of intermediate representation according to their
mutual information (MI) with labels, as the network trained with IB provides
easily distinguishable MI for its features. Experimental results show that our
method can be naturally combined with adversarial training and provides
consistently better accuracy on new adversarial examples. Our method improves
the accuracy by an average of 3.07% against five adversarial attacks for the
VGG16 network, trained with three adversarial training benchmarks and the
CIFAR-10 dataset. In addition, our method also provides good robustness for
undefended methods, such as training with cross-entropy loss only. Finally, in
the absence of adversarial training, the VGG16 network trained using our method
and the CIFAR-10 dataset reaches an accuracy of 35.86% against PGD examples,
while using all layers reaches 25.61% accuracy.
