Learning Discriminative Palmprint Anti-Spoofing Features via High-Frequency Spoofing Regions Adaptation

Recently, the majority of palmprint recognition studies have focused on feature extraction while neglecting security issues. Among the various attack types, spoofing attack poses a significant threat due to high success rates and minimal technical requirements. In this study, we explore the differences between real and fake palmprint images. Based on these differences, we propose the concept of ‘high-frequency spoofing regions’ to capture key discriminative spoofing clues. Specifically, the high-frequency spoofing regions adaptation (HFSRA) model is proposed to address palmprint anti-spoofing. The HFSRA consists of two key modules: the texture analysis module (TAM) and the spoofing attention module (SAM). In particular, the TAM divides the input feature map into several patches and evaluates the texture distribution within each patch. Next, the SAM dynamically constructs an attention map by mapping the texture distribution to an attention weight matrix. This adaptive structure forces the model to focus on high-frequency spoofing regions, which improves the model's ability to extract meaningful spoofing clues effectively. Furthermore, we establish three experimental protocols for evaluating the performance of palmprint anti-spoofing models. These protocols provide a standardized evaluation framework for future studies. Extensive experiments conducted under these protocols demonstrate the effectiveness and competitiveness of HFSRA.