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- W3128391794 abstract "To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback. Existing black-box attacks generally suffer from high query complexity, especially when only the top-1 decision (i.e., the hard-label prediction) of the victim model is available. In this paper, we propose a novel hard-label black-box attack named Policy-Driven Attack, to reduce the query complexity. Our core idea is to learn promising search directions of the adversarial examples using a well-designed policy network in a novel reinforcement learning formulation, in which the queries become more sensible. Experimental results demonstrate that our method can significantly reduce the query complexity in comparison with existing state-of-the-art hard-label black-box attacks on various image classification benchmark datasets. Anonymous code and models for reproducing our results are available in the supplementary material." @default.
- W3128391794 created "2021-02-15" @default.
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- W3128391794 date "2021-05-03" @default.
- W3128391794 modified "2023-09-27" @default.
- W3128391794 title "Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples" @default.
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