S⁴ST shows that dimensionally consistent scaling with low-redundancy complementary transforms achieves state-of-the-art data-free transferable targeted attacks by exploiting visual data's multi-scale nature.
Deep residual learning for image recognition,
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S$^4$ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack
S⁴ST shows that dimensionally consistent scaling with low-redundancy complementary transforms achieves state-of-the-art data-free transferable targeted attacks by exploiting visual data's multi-scale nature.