Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
CoRR(2023)
摘要
We propose a probabilistic perspective on adversarial examples. This
perspective allows us to view geometric restrictions on adversarial examples as
distributions, enabling a seamless shift towards data-driven, semantic
constraints. Building on this foundation, we present a method for creating
semantics-aware adversarial examples in a principle way. Leveraging the
advanced generalization capabilities of contemporary probabilistic generative
models, our method produces adversarial perturbations that maintain the
original image's semantics. Moreover, it offers users the flexibility to inject
their own understanding of semantics into the adversarial examples. Our
empirical findings indicate that the proposed methods achieve enhanced
transferability and higher success rates in circumventing adversarial defense
mechanisms, while maintaining a low detection rate by human observers.
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关键词
probabilistic
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