Face Presentation Attack Detection
Overview
Face Presentation Attack Detection (FacePAD) aims to distinguish genuine live presentations from artifacts or manipulated inputs used to fool biometric systems. Methods range from texture and motion analysis with convolutional networks to Vision Transformers, frequency-based features, and multimodal models that use text, tools, or structured attack knowledge. Researchers evaluate these systems on benchmarks such as OULU-NPU, SiW, and Replay-Attack, including tests that train on one dataset or attack type and evaluate on another. A central challenge is that strong results on familiar data often do not transfer to new sensors, attack methods, demographic groups, or real-time settings.
Latest papers
22 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.