S4ST: A Strong, Self-Transferable, faSt, and Simple Scale Transformation for Data-Free Transferable Targeted Attack

Summary

This paper studies transferable targeted attacks on image models that work without victim-model training data or black-box feedback. The authors introduce two blind estimation measures, self-alignment and self-transferability, to assess each image transformation. They find that simple scaling transformations uniquely enhance targeted transferability, and they build S$^{4}$4ST, a scale transformation method, which achieves state-of-the-art effectiveness-efficiency balance across architectures, training distributions and tasks.

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