The ISC International Journal of Information Security

Abstract Advanced fingerprint spoofing attacks often threaten biometric authentication systems, such as the presence of non-contiguous minutiae to form a double-identity fingerprint. This research paper offers a deep learning system based on MobileNet to differentiate between authentic and anomalous fingerprints. The SOCOFing dataset (in the public domain) with real fingerprints and synthetically deformed samples at three levels of difficulty (easy, medium, and hard) was experimented with, which is used as a reference point in assessing the performance of spoof detection. In order to enhance the generalization, data augmentation and transfer learning were utilized, which helped in increasing the resilience of the model to different fingerprint modifications. The overall test accuracy of the MobileNet-based model was 88%, which declined with the case of medium and hard alterations, which suggests the inability to detect extremely complicated spoofing patterns. However, the model is efficient and has low computational expense, thus it can form a viable yet lightweight real-time biometric verification architecture. The future research ought to cover more sophisticated architectures, cross-dataset testing, and feature-level analysis in order to enhance the detection of difficult forms of spoofing.