  Recent studies have highlighted that deep neural networks (DNNs) are
vulnerable to adversarial examples. In this paper, we improve the robustness of
DNNs by utilizing techniques of Distance Metric Learning. Specifically, we
incorporate Triplet Loss, one of the most popular Distance Metric Learning
methods, into the framework of adversarial training. Our proposed algorithm,
Adversarial Training with Triplet Loss (AT$^2$L), substitutes the adversarial
example against the current model for the anchor of triplet loss to effectively
smooth the classification boundary. Furthermore, we propose an ensemble version
of AT$^2$L, which aggregates different attack methods and model structures for
better defense effects. Our empirical studies verify that the proposed approach
can significantly improve the robustness of DNNs without sacrificing accuracy.
Finally, we demonstrate that our specially designed triplet loss can also be
used as a regularization term to enhance other defense methods.
