A Multimodal Framework for Realtime Antispoofing and Identity Verification

Identity verification systems are increasingly exposed to spoofing attacks such as printed images, replayed videos, and synthetic media, which exploit vulnerabilities in unimodal biometric authentication.This work presents a multimodal deep learning framework for real-time anti-spoofing and identity verification by jointly leveraging facial, voice, and behavioral biometric signals.The proposed system integrates convolutional neural networks for spatial feature extraction with recurrent models to capture temporal inconsistencies indicative of spoofing attempts.By fusing complementary modalities at the score level, the framework enhances discrimination between genuine and fraudulent inputs while improving robustness under varying environmental conditions.The model is trained on preprocessed multimodal datasets with normalization and temporal alignment to ensure stable convergence and effective generalization.Experimental evaluations demonstrate that the proposed multimodal approach achieves higher verification accuracy, reduced false acceptance rates, and improved spoof detection performance compared to unimodal and traditional biometric systems.The results emphasize the effectiveness of deep learning-driven multimodal pipelines in strengthening secure authentication mechanisms for real-time access control and digital identity applications.