A Closer Look at the Adversarial Robustness of Information Bottleneck Models

Abstract

We study the adversarial robustness of information bottleneck models for classification. Previous works showed that the robustness of models trained with information bottlenecks can improve upon adversarial training. Our evaluation under a diverse range of white-box $l_{\infty }$ attacks suggests that information bottlenecks alone are not a strong defense strategy, and that previous results were likely influenced by gradient obfuscation.

References in corpus (2)

• On Evaluating Adversarial Robustness
• An Alternative Surrogate Loss for PGD-based Adversarial Testing