Adaptive Resilience Framework Using Dynamic Feature Fusion for Robust Fingerprint Biometrics Against Adversarial Perturbations

This paper presents the Adaptive Resilience Fingerprint Defense (ARFD), a novel framework to enhance fingerprint biometric systems’ robustness against adversarial attacks like FGSM and PGD. ARFD integrates Dynamic Feature Fusion (DFF) for real-time feature weight recalibration and Multi-Scale Feature Ensemble (MFE) for multi-resolution analysis. This two-pronged strategy effectively mitigates adversarial perturbations, achieving superior accuracy and reducing false acceptance and rejection rates. Experimental results demonstrate ARFD’s significant advancements in biometric security, providing an adaptive and resilient defense mechanism.