轻量级多模态反伪造框架，支持语音人脸等生物特征防骗
LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security
- 设计跨模态对齐损失，提升不同生物特征间的检测一致性
- 仅600万参数，在7个数据集上平均误报率降低1.36%以上
- 适合边缘设备部署，适用于多种生物识别系统安全防护
生物特征认证系统在关键应用中日益普及，但仍易受伪造攻击。现有研究多聚焦于单一模态的反伪造技术，构建跨多种生物特征、资源高效的统一解决方案仍具挑战。为此，我们提出LitMAS——一种轻量且通用的多模态反伪造框架，用于检测基于语音、人脸、虹膜和指纹的生物特征系统中的伪造攻击。其核心是模态对齐集中损失（Modality-Aligned Concentration Loss），在增强类间可分性的同时保持跨模态一致性，实现对多种生物特征的鲁棒伪造检测。仅需600万参数，LitMAS在七个数据集上的平均等错误率（EER）相比现有最优方法提升1.36%，展现出高效率、强泛化性及边缘部署适用性。代码与训练模型已公开于https://github.com/IAB-IITJ/LitMAS。
原文摘要 · Abstract (English)
Biometric authentication systems are increasingly being deployed in critical applications, but they remain susceptible to spoofing. Since most of the research efforts focus on modality-specific anti-spoofing techniques, building a unified, resource-efficient solution across multiple biometric modalities remains a challenge. To address this, we propose LitMAS, a $\textbf{Li}$gh$\textbf{t}$ weight and generalizable $\textbf{M}$ulti-modal $\textbf{A}$nti-$\textbf{S}$poofing framework designed to detect spoofing attacks in speech, face, iris, and fingerprint-based biometric systems. At the core of LitMAS is a Modality-Aligned Concentration Loss, which enhances inter-class separability while preserving cross-modal consistency and enabling robust spoof detection across diverse biometric traits. With just 6M parameters, LitMAS surpasses state-of-the-art methods by $1.36\%$ in average EER across seven datasets, demonstrating high efficiency, strong generalizability, and suitability for edge deployment. Code and trained models are available at https://github.com/IAB-IITJ/LitMAS.
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