On Pruning Adversarially Robust Neural Networks

In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size (often millions of parameters). While the research community has extensively explored the use of robust training and network pruning \emph{independently} to address one of these challenges, we show that integrating existing pruning techniques with multiple types of robust training techniques, including verifiably robust training, leads to poor robust accuracy even though such techniques can preserve high regular accuracy. We further demonstrate that making pruning techniques aware of the robust learning objective can lead to a large improvement in performance. We realize this insight by formulating the pruning objective as an empirical risk minimization problem which is then solved using SGD. We demonstrate the success of the proposed pruning technique across CIFAR-10, SVHN, and ImageNet dataset with four different robust training techniques: iterative adversarial training, randomized smoothing, MixTrain, and CROWN-IBP. Specifically, at 99\% connection pruning ratio, we achieve gains up to 3.2, 10.0, and 17.8 percentage points in robust accuracy under state-of-the-art adversarial attacks for ImageNet, CIFAR-10, and SVHN dataset, respectively. Our code and compressed networks are publicly available at https://github.com/inspire-group/compactness-robustness