Logit Pairing Methods Can Fool Gradient-Based Attacks
Abstract
Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make the gradient-based optimization problem of crafting adversarial examples harder without providing actual robustness. We find that Adversarial Logit Pairing (ALP) may indeed provide robustness against adversarial examples, especially when combined with adversarial training, and we examine it in a variety of settings. However, the increase in adversarial accuracy is much smaller than previously claimed. Finally, our results suggest that the evaluation against an iterative PGD attack relies heavily on the parameters used and may result in false conclusions regarding robustness of a model.
Keywords
Cite
@article{arxiv.1810.12042,
title = {Logit Pairing Methods Can Fool Gradient-Based Attacks},
author = {Marius Mosbach and Maksym Andriushchenko and Thomas Trost and Matthias Hein and Dietrich Klakow},
journal= {arXiv preprint arXiv:1810.12042},
year = {2019}
}
Comments
Accepted to NeurIPS 2018 Workshop on Security in Machine Learning