Metric Learning for Adversarial Robustness

TL;DR

This paper introduces a metric learning-based regularization method to enhance deep network robustness against adversarial attacks, improving accuracy and detection of adversarial samples.

Contribution

It proposes a novel metric learning approach to regularize representations under attack, increasing robustness and enabling detection of unseen adversarial samples.

Findings

Robustness accuracy improved by up to 4%.

Detection efficiency increased by up to 6% in AUC score.

Representation shifts closer to false class under PGD attack are mitigated.

Abstract

Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. Motivated by this observation, we propose to regularize the representation space under attack with metric learning to produce more robust classifiers. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior work. The code of our work is available at https://github.com/columbia/Metric_Learning_Adversarial_Robustness.