AI RESEARCH
DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
arXiv CS.LG
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ArXi:2508.13309v3 Announce Type: replace-cross Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such norm-bounded examples often fail to align well with human perception, and only recently have a few methods begun specifically exploring perceptually aligned adversarial examples. Moreover, it remains unclear whether insights from Lp-constrained attacks can be effectively leveraged to improve perceptual efficacy. In this paper, we