June 6, 2024, 4:49 a.m. | Jun Liu, Jiantao Zhou, Jiandian Zeng, Jinyu Tian

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.03017v1 Announce Type: new
Abstract: This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on a \textit{Hierarchical} \textbf{Di}sentangled \textbf{F}eature space and \textit{cross domain}, called \textbf{DifAttack++}, which differs significantly from the existing ones operating over the entire feature space. Specifically, DifAttack++ firstly disentangles an image's latent feature into an \textit{adversarial feature} (AF) and a \textit{visual feature} (VF) via an autoencoder equipped with our specially designed …

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