Design of an Adaptive and Iterative Multi-Modal Transformer-Based Biometric Security Framework for Robust Authentication and Spoof Detection

As more people rely on biometrics in conjunction with traditional identity authentication systems, attacks like deepfakes and adversarial techniques have emerged as prominent threats in several identity verification systems. Traditional unimodal and even static multimodal schemes are generally found ineffective in the face of new attacks because unimodality leaves them vulnerable to different adversarial manipulations, cannot verify continuously during the use phase, and lack adaptability to change. Therefore, in light of these limitations, we present an adaptive, robust, and privacy-preserving biometric security framework based on a combination of various transformer-based models with ensemble learning strategies. Our work, the Adaptive Multi-Modal Feature Fusion Transformer (AMFFT), dynamically integrates attention-based facial, voice, and fingerprint features to optimize security through a context-oriented fusion application. To assure enhanced privacy and robustness, we will also integrate Differentially Private Adversarial Training (DPAT), interested in lessening the effect of model inversion and spoofing using adversarial techniques. Therefore, our Spoof-Resistant Multimodal Attention Transformer (SMA-Transformer) detects deepfake and synthetic attacks by consistency between modalities, ensuring the co-operation of biometric signals. In addition to that, the Ensemble Learning with Zero-Trust Verification Model (EZV-Model) is responsible for continuous authentication by real-time analysis of biometrics scores and behavior traits. Finally, the Real-Time Behavioral Biometric Security Model (RBB-Sec) can detect advanced impersonation scenarios based on micro-expressions, keystroke dynamics, and voice stress patterns. In combination with the above techniques, the proposed framework guarantees a significant improvement in performance regarding authentication accuracy (≥99.5%), spoof detection (≥99.3%), and adversarial robustness (≤1.2% evasion rate), while maintaining low false rejection rates (≤1.5%). By integrating adaptive biometric fusion, deepfake-resistant verification, and zero-trust-based continuous authentication, this work lays an advanced security paradigm against emerging cyber threats for biometric security systems.