Domain-Generalized Object Anti-Spoofing: Bridging Gaps and Patch Selection for Robust Detection Across Domains

In online applications, significant risks exist in peer-to-peer transactions due to malicious behaviors of arbitrary users, such as taking advantage of manipulated images or impersonating others using recaptured images. Moreover, recent advancements in display screens and imaging devices have made it increasingly challenging to distinguish such spoofing images from the naked eye. However, a lack of datasets for object anti-spoofing significantly hinders the practical implementation of object anti-spoofing techniques compared to facial anti-spoofing tasks. To address this data scarcity issue for object anti-spoofing, we propose a method that utilizes face anti-spoofing images for training. Our approach leverages low-rank adaptation, employing fine-tuning with downstream tasks of large language models to facilitate domain transition between faces and generic objects. We also analyze a power spectrum to select useful patches for spoofing detection and introduce a patch-based learning method to effectively capture spoofing patterns. Lastly, we present a novel protocol for assessing domain generalization in the generic object anti-spoofing task. Our model demonstrates state-of-the-art generalization performance compared to existing object anti-spoofing models, surpassing even those simply augmented with face datasets.