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

TL;DR

LitMAS is a lightweight, multi-modal anti-spoofing framework that effectively detects biometric spoofing across speech, face, iris, and fingerprint modalities with high accuracy and resource efficiency.

Contribution

The paper introduces LitMAS, a novel unified anti-spoofing framework with a new loss function, achieving superior performance with only 6 million parameters.

Findings

Surpasses state-of-the-art by 1.36% in average EER

Effective across multiple biometric modalities

Highly efficient with 6M parameters

Abstract

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.