Rethinking Fake Adversarial Examples for Single-Step Adversarial Training

Summary

Researchers examine why single-step adversarial training, a cheaper alternative to standard adversarial training, often suffers catastrophic overfitting under larger perturbations. They identify "fakers," single-step adversarial examples that are learned and correctly classified yet fail to expose true model vulnerabilities, and that degrade robustness, resist learning and diverge strongly from their clean counterparts. They propose FAST, which adjusts label smoothing by learning difficulty and adds a weak-effect auxiliary sample, reporting superior clean accuracy and robustness across various attacks.

Topics

• Adversarial machine learning

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