Modality-Shared Anti-Spoofing for Face and Fingerprint

Abstract

Multi-modal anti-spoofing aims to differentiate live users from spoofing attacks using multiple biometric modalities during model training. While existing anti-spoofing methods often incorporate just one biometric modality, the effectiveness of attacking two or more biometric traits remains questionable. In this work, we introduce the multi-modal anti-spoofing approach to detect spoofing attacks across face and fingerprint. Our framework is built around an Angular Margin Loss (ArcFace) that increases interclass separation without disrupting cross-modal alignment, which enables reliable spoof detection across both face and fingerprint biometric characteristics. Moreover, to enhance model generalization against unseen spoof attacks, we include three adversarial attacks (i.e., FGSM, PGD, DeepFool) to evaluate our system. Extensive experiments on multi-modal benchmarks show that the proposed method not only significantly outperforms previous anti-spoofing methods but also uniquely offers the ability to handle potential attack types.