  Upon the discovery of adversarial attacks, robust models have become
obligatory for deep learning-based systems. Adversarial training with
first-order attacks has been one of the most effective defenses against
adversarial perturbations to this day. The majority of the adversarial training
approaches focus on iteratively perturbing each pixel with the gradient of the
loss function with respect to the input image. However, the adversarial
training with gradient-based attacks lacks diversity and does not generalize
well to natural images and various attacks. This study presents a robust
training algorithm where the adversarial perturbations are automatically
synthesized from a random vector using a generator network. The classifier is
trained with cross-entropy loss regularized with the optimal transport distance
between the representations of the natural and synthesized adversarial samples.
Unlike prevailing generative defenses, the proposed one-step attack generation
framework synthesizes diverse perturbations without utilizing gradient of the
classifier's loss. Experimental results show that the proposed approach attains
comparable robustness with various gradient-based and generative robust
training techniques on CIFAR10, CIFAR100, and SVHN datasets. In addition,
compared to the baselines, the proposed robust training framework generalizes
well to the natural samples. Code and trained models will be made publicly
available.
