Adversarial Robustness with Non-uniform PerturbationsDownload PDF

21 May 2021, 20:45 (modified: 29 Oct 2021, 16:55)NeurIPS 2021 PosterReaders: Everyone
Keywords: Adversarial robustness, adversarial attacks, projected gradient descent, certified robustness, randomized smoothing
TL;DR: Adversarial training with non-uniform perturbations across the features provides better robustness against the real-world attacks than the uniform approach.
Abstract: Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Prior work mainly focus on crafting adversarial examples (AEs) with small uniform norm-bounded perturbations across features to maintain the requirement of imperceptibility. However, uniform perturbations do not result in realistic AEs in domains such as malware, finance, and social networks. For these types of applications, features typically have some semantically meaningful dependencies. The key idea of our proposed approach is to enable non-uniform perturbations that can adequately represent these feature dependencies during adversarial training. We propose using characteristics of the empirical data distribution, both on correlations between the features and the importance of the features themselves. Using experimental datasets for malware classification, credit risk prediction, and spam detection, we show that our approach is more robust to real-world attacks. Finally, we present robustness certification utilizing non-uniform perturbation bounds, and show that non-uniform bounds achieve better certification.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
Code: https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations
44 Replies

Loading