Deep Learning-based Detection of Synthetic Fingerprint Images
Keywords:
Altered fingerprints, Biometric security, Convolutional neural networks, Machine learning, SOCOFing datasetAbstract
Fingerprint biometric systems are a staple of modern security deployments. However, they are still susceptible to spoofing and tampering attacks that compromise system integrity. This paper provides an enduring machine learning approach able to accurately spot spoofed fingerprints. Leveraging the publicly available SOCOFing dataset of 6,000 real and 49,270 spoofed fingerprint images, this paper presents a deep learning approach utilizing Convolutional Neural Networks (CNNs). The model is trained to effectively derive and process significant fingerprint features, including ridge patterns, minutiae points, and texture information. Experimental results indicate that the CNN performs better in discriminating real and forged fingerprints and therefore increases the consistency of biometric authentication systems. Our work further emphasizes the potential of Artificial Intelligence (AI) in enhancing biometric security by automatically identifying high-level forgeries. The proposed methodology not only improves forensic performance but also offers a foundation for real-time deployment in authentication systems. This work highlights the value of scalable AI in biometric solutions and offers directions for further research, such as the optimization of CNN models as well as integrating hybrid models for generalization performance and detection resilience improvement.
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