FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
Guray Ozgur, Eduarda Caldeira, Tahar Chettaoui, Fadi Boutros, Raghavendra Ramachandra, Naser Damer
Abstract
Although face recognition systems have seen a massive performance enhancement in recent years, they are still targeted by threats such as presentation attacks, leading to the need for generalizable presentation attack detection (PAD) algorithms. Current PAD solutions suffer from two main problems: low generalization to unknown scenarios and large training data requirements. Foundation models (FM) are pretrained on extensive datasets, achieving remarkable results when generalizing to unseen domains and allowing for efficient task-specific adaption even in low data availability settings. This is one of the first works to recognize the potential of FMs and adapt them for the downstream task of PAD. The FM under consideration is adapted with LoRA weights while simultaneously training a classification header. The resultant architecture, FoundPAD, is highly generalizable to unseen domains, achieving competitive results in several settings under different data availability scenarios and even when using synthetic training data. To encourage reproducibility and facilitate further research in PAD, we publicly release the implementation of FoundPAD at https://github.com/gurayozgur/FoundPAD.