Metric Learning for Adversarial Robustness
This addresses adversarial robustness for deep learning systems, offering incremental improvements in accuracy and detection over prior work.
The paper tackles the problem of deep networks being vulnerable to adversarial attacks by analyzing how attacks shift internal representations toward false classes, and proposes using metric learning to regularize the representation space, resulting in up to 4% improvement in robustness accuracy and up to 6% improvement in detection efficiency.
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.