Presentation Attack Detection

Presentation attack detection (PAD) aims to secure biometric systems by identifying fraudulent attempts to bypass authentication using fake biometric data (e.g., fake fingerprints, photos, or videos). Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating multi-modal data (e.g., combining visual and auditory information) or handcrafted features to improve accuracy and generalization across diverse attack types and datasets. The development of robust and generalizable PAD methods is crucial for ensuring the security and reliability of biometric authentication in various applications, from access control to online identity verification.