[2604.06987] CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.06987 (cs) [Submitted on 8 Apr 2026] Title:CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models Authors:Renyang Liu, Jiale Li, Jie Zhang, Cong Wu, Xiaojun Jia, Shuxin Li, Wei Zhou, Kwok-Yan Lam, See-kiong Ng View a PDF of the paper titled CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models, by Renyang Liu and 8 other authors View PDF HTML (experimental) Abstract:Palmprint recognition is deployed in security-critical applications, including access control and palm-based payment, due to its contactless acquisition and highly discriminative ridge-and-crease textures. However, the robustness of deep palmprint recognition systems against physically realizable attacks remains insufficiently understood. Existing studies are largely confined to the digital setting and do not adequately account for the texture-dominant nature of palmprint recognition or the distortions introduced during physical acquisition. To address this gap, we propose CAAP, a capture-aware adversarial patch framework for palmprint recognition. CAAP learns a universal patch that can be reused across inputs while remaining effective under realistic acquisition variation. To match the structural characteristics of palmprints, the framework adopts a cross-shaped patch topology, which enlarges spatial coverage under a fixed pixel budget and more effectively disrupts long-range texture continuity....