Perspective-Invariant Attack With Enhanced Transferability of Adversarial Examples

Summary

This research paper proposes the Perspective-Invariant Attack (PIA), which improves the transferability of adversarial examples between black-box DNN models. PIA uses a multi-DOF vertex sampling strategy covering perspective transformations from 2-DOF translation to 8-DOF projective mapping. The authors also present PIA-Mix, an extension that combines this transformation with auxiliary methods, and report that both outperform state-of-the-art transfer-based attacks across DNN architectures, defenses and multimodal LLMs.

Topics

• Adversarial machine learning

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