Improving the Robustness of Deep Neural Networks via
Adversarial Training with Triplet Loss
Abstract
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 (AT2L), substitutes the adversarial example against the current model for the anchor of triplet loss to effectively smooth the classi-
fication boundary. Furthermore, we propose an ensemble version of AT2L, 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