Seeking Flat Minima Over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation

Summary

This paper presents a theoretical framework for transfer-based black-box adversarial attacks, deriving a transferability bound that links adversarial transferability to flat minima over a surrogate model set and the adversarial model discrepancy. Based on this bound, the authors build a surrogate model set with diverse adversarial vulnerabilities and generate a model-Diversity-compatible Reverse Adversarial Perturbation (DRAP). Experiments on the NIPS2017 and CIFAR-10 datasets against various target models show the proposed attack is effective.

Topics

• Adversarial machine learning

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