LAUSR

Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the $l_{p}$ space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance. Unlike previous approaches, we use an unbalanced optimal transport formulation which is naturally suited for images. We first compute an adversarial sample using a gradient step and then project the resultant image into Wasserstein ball with respect to original sample. The attack introduces perturbation in the form of pixel mass distribution which is guided by a cost metric. Elaborate experiments on MNIST, Fashion-MNIST, CIFAR-10 and Tiny ImageNet demonstrate a sharp decrease in the performance of state-of-art classifiers. We also perform experiments with adversarially trained classifiers and show that our system achieves superior performance in terms of adversarial defense against several state-of-art attacks. Our code and pre-trained models are available at https://bit.ly/2SQBR4E.