Adaptive multimodal liveness detection framework for biometric authentication against spoofing and deepfake attacks

Abstract

With the explosive growth of generative AI technologies, the threat posed by spoofing attacks and deepfake attacks on biometric authentication systems continues to grow. Traditional biometric systems rely on visual presentation and are subject to various types of presentation attacks such as replay attacks, printed photographs, and synthetic deepfake identities. This paper presents an Adaptive Multimodal Liveness Detection Framework (AMLF) to increase the strength of biometric identity systems and reduce their vulnerability to both spoofing and deepfake attacks. The framework also aims to reduce the computational costs of performing liveness detection on resource-constrained edge devices. The proposed framework uses three techniques for liveness detection across multiple biometric modalities (face, fingerprint, and iris): spatial texture analysis, temporal biometric signals, and frequency-domain artifacts. A lightweight, deep neural network architecture is used for the multimodal liveness detection process on edge devices and enables real-time liveness detection. A series of experiments conducted on publicly available datasets (FaceForensics++ , CASIA-Iris-V4, MSU-MFSD, and FVC2006) demonstrate that the proposed AMLF considerably increases the accuracy of detecting spoofing attacks over existing approaches based on deep learning methods while significantly reducing the computational costs associated with these methods. Overall, the AMLF framework provides a highly effective means to improve the capability of next generation biometric authentication systems, particularly in an edge computing environment.