Author

P. Lorenz

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Preprint Aug 2026

LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection

Face presentation attack detection (PAD) aims to reliably detect a wide range of presentation attacks. While PAD methods achieve strong performance within individual datasets, their performance degrades under cross-dataset evaluation. Variations in sensors or lighting conditions can reduce the effectiveness of detector...

P. Lorenz, Anjith George, Marcel Sébastien · 0 citations

Jul 2026

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 funda...

P. Lorenz, Anjith George, Sébastien Marcel · arXiv.org · 1 citation

Preprint Aug 2026

Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

The experiments show that FMs and MLLMs can achieve significant performance for PAD and MAD and the fine-tuned models achieve state-of-the-art detection performance in cross-dataset evaluation, indicating that general-purpose pretrained representations carry substantial attack-relevant information.

H. O. Shahreza, A. H. Khan, P. Lorenz et al. · 0 citations