Fingerprint Presentation Attack

Fingerprint presentation attack detection (PAD) aims to secure fingerprint authentication systems against spoofing attempts using fake fingerprints. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and diffusion models, often incorporating techniques like feature fusion (combining handcrafted and learned features) and attention mechanisms to improve accuracy and generalization across diverse attack methods. The development of robust PAD methods is crucial for maintaining the security and reliability of fingerprint-based authentication systems across various applications, from border control to mobile device security, and ongoing competitions like LivDet benchmark progress in this field.