LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security

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 atthis https URL.

@article{gorthi2025_2506.06759,
  title={ LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security },
  author={ Nidheesh Gorthi and Kartik Thakral and Rishabh Ranjan and Richa Singh and Mayank Vatsa },
  journal={arXiv preprint arXiv:2506.06759},
  year={ 2025 }
}