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- W4376653638 abstract "Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space. Despite some achievements in transferability and attack success rate, the crafted adversarial examples are easily perceived by human eyes. Towards visual imperceptibility, some recent works explore unrestricted attacks without $L_p$-norm constraints, yet lacking transferability of attacking black-box models. In this work, we propose a novel imperceptible and transferable attack by leveraging both the generative and discriminative power of diffusion models. Specifically, instead of direct manipulation in pixel space, we craft perturbations in latent space of diffusion models. Combined with well-designed content-preserving structures, we can generate human-insensitive perturbations embedded with semantic clues. For better transferability, we further deceive the diffusion model which can be viewed as an additional recognition surrogate, by distracting its attention away from the target regions. To our knowledge, our proposed method, DiffAttack, is the first that introduces diffusion models into adversarial attack field. Extensive experiments on various model structures (including CNNs, Transformers, MLPs) and defense methods have demonstrated our superiority over other attack methods." @default.
- W4376653638 created "2023-05-17" @default.
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- W4376653638 date "2023-05-14" @default.
- W4376653638 modified "2023-10-18" @default.
- W4376653638 title "Diffusion Models for Imperceptible and Transferable Adversarial Attack" @default.
- W4376653638 doi "https://doi.org/10.48550/arxiv.2305.08192" @default.
- W4376653638 hasPublicationYear "2023" @default.
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