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Deep neural networks are capable of training fast and generalizing wellwithin many domains. Despite their promising performance, deep networks haveshown sensitivities to perturbations of their inputs (e.g., adversarialexamples) and their learned feature representations are often difficult tointerpret, raising concerns about their true capability and trustworthiness.Recent work in adversarial training, a form of robust optimization in which themodel is optimized against adversarial examples, demonstrates the ability toimprove performance sensitivities to perturbations and yield featurerepresentations that are more interpretable. Adversarial training, however,comes with an increased computational cost over that of standard (i.e.,nonrobust) training, rendering it impractical for use in large-scale problems.Recent work suggests that a fast approximation to adversarial training showspromise for reducing training time and maintaining robustness in the presenceof perturbations bounded by the infinity norm. In this work, we demonstratethat this approach extends to the Euclidean norm and preserves thehuman-aligned feature representations that are common for robust models.Additionally, we show that using a distributed training scheme can furtherreduce the time to train robust deep networks. Fast adversarial training is apromising approach that will provide increased security and explainability inmachine learning applications for which robust optimization was previouslythought to be impractical.

Fast Training of Deep Neural Networks Robust to Adversarial Perturbations