Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset. The scarcity of large-scale labeled data motivates adapting pretrained vision models rather than training task-specific architectures from scratch, raising a fundamental question: do general-purpose vision foundation models encode PAD-relevant information accessible with minimal task-specific training? To investigate, we systematically evaluate 24 frozen encoders, including self-supervised vision transformers, vision-language encoders, and supervised CNNs, using a unified linear-probing protocol on the MCIO benchmark (MSU-MFSD, CASIA-...
| Authors | Peter Lorenz, Anjith George, Sébastien Marcel |
|---|---|
| Primary category | cs.LG |
| Published | 29 July 2026 |
| Last updated at source | 30 July 2026 |
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