Fingerprint Spoofing Attacks and their Deep Learning-enabled Remediation: State-of-the-art, Taxonomy, and Future Directions

With the rapid increase in fingerprint-based identification systems, there are growing concerns related to spoofs or artificially created fingerprint substantiations that can breach the security of these systems. Several issues associated with the susceptibility of fingerprint authentication systems to spoofing attacks are being underlined in recent research works. To produce spoof artefacts, easily accessible materials like Play-Doh, Gelatin, Wood glue and Ecoflex are etched with ridges of fingerprint modality and presented to the biometric device to circumvent the sensor module. A number of anti-spoofing techniques that incorporate the potent fingerprint features are being developed to countermeasure the fingerprint presentation attacks (PAs). These strategies are intended to thwart an adversary from obtaining unauthorized access to the sensitive information that the fingerprint recognition system is supposed to protect. This paper examines fingerprint presentation attack detection (PAD) mechanisms in depth, with an emphasis on deep learning-based solutions. The review clearly reveals that software-based PAD techniques are more efficient as compared to their hardware counterparts in terms of performance metrics. However, designing fingerprint PAD techniques which provide better performance in cross-datasets and cross-sensor environment still remains an open research problem. Besides, requirement of large data volumes for training and the emergence of novel and easily accessible materials for creating spoof artefacts is still an open research challenge.