Revolutionizing Biometric Security: Advanced Deep Learning Strategies for Fingerprint Anti-Spoofing in High-Risk Applications

The mass use of biometric authentication renders fingerprint recognition vulnerable to spoofing attacks and increases the potential threats to security in high-risk applications such as banking, border control, and critical infrastructure. Existing fingerprint anti-spoofing methods such as texture analysis and pulse detection fail to perform satisfactorily in the presence of sophisticated spoofing techniques. Deep-learning-based framework developed to enhance spoof detection of fingerprint by using GANs for augmentation of Convolutional Neural Networks. Convolutional layer of the net captures complex ridge patterns and pores distributions, followed by a fully connected layer consisting of GAN for generating new spoof fingerprints augmenting the datasets and improving models' robustness. In fact, the SOCOFing dataset was employed to train this proposed system that obtained a classification accuracy of 92.8%, FAR 0.7%, and FRR 1.2%. The comparative study reveals that the hybrid CNN-GAN model shows better generalization against unseen spoofing attacks when compared with all the existing techniques. This means that the real-time inference capability of the model, with an average prediction time of 6 milliseconds per fingerprint, makes it viable for practical deployment. In conclusion, this study contributes to the advancement of secure and reliable biometric authentication by providing a scalable foundation for future innovations in fingerprint anti-spoofing.