Adversarial Examples Are Not Real Features

TL;DR

This paper challenges the idea that adversarial examples are based on useful non-robust features, showing they are more like shortcuts that do not transfer well across different learning paradigms and do not ensure robustness.

Contribution

The study re-examines the role of non-robust features across multiple learning paradigms, revealing their limited usefulness and their nature as paradigm-specific shortcuts.

Findings

Non-robust features transfer poorly across different paradigms.

Naturally trained robust features are non-robust under AutoAttack.

Non-robust features are more like shortcuts than genuinely useful features.

Abstract

The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerability from a data perspective by showing that one can extract non-robust features from adversarial examples and these features alone are useful for classification. However, the explanation remains quite counter-intuitive since non-robust features are mostly noise features to humans. In this paper, we re-examine the theory from a larger context by incorporating multiple learning paradigms. Notably, we find that contrary to their good usefulness under supervised learning, non-robust features attain poor usefulness when transferred to other self-supervised learning paradigms, such as contrastive learning, masked image modeling, and diffusion models. It reveals that non-robust features are not really as useful as robust or natural features that enjoy good transferability between these paradigms. Meanwhile, for robustness, we also show that naturally trained encoders from robust features are largely non-robust under AutoAttack. Our cross-paradigm examination suggests that the non-robust features are not really useful but more like paradigm-wise shortcuts, and robust features alone might be insufficient to attain reliable model robustness. Code is available at \url{https://github.com/PKU-ML/AdvNotRealFeatures}.