Adversarial Deep Metric Learning

Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha

Keywords: Deep metric learning, adversarial robustness, adversarial examples, adversarial perturbations, adversarial training

Abstract: Learning a distance metric between pairs of examples is widely important for various tasks. Deep Metric Learning (DML) utilizes deep neural network architectures to learn semantic feature embeddings where the distance between similar examples is close and dissimilar examples are far. While the underlying neural networks produce good accuracy on naturally occurring samples, they are vulnerable to adversarially-perturbed samples that can reduce their accuracy. To create robust versions of DML models, we introduce a robust training approach. A key challenge is that metric losses are not independent --- they depend on all samples in a mini-batch. This sensitivity to samples, if not accounted for, can lead to incorrect robust training. To the best of our knowledge, we are the first to systematically analyze this dependence effect and propose a principled approach for robust training of deep metric learning networks that accounts for the nuances of metric losses. Using experiments on three popular datasets in metric learning, we demonstrate the DML models trained using our techniques display robustness against strong iterative attacks while their performance on unperturbed (natural) samples remains largely unaffected.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

One-sentence Summary: We conduct a systematic exploration of the robustness of Deep Metric Learning models, and propose a method to improve their robustness towards adversarial perturbations.

Reviewed Version (pdf): https://openreview.net/references/pdf?id=OaqY0acBLe