这篇论文用32个模型实测告诉你，LoRA不是万能药，人脸防伪跨数据集还是得看预训练底子和数据。
该研究系统评估了32个基础模型在人脸呈现攻击检测（PAD）中的表现，涵盖不同架构和训练流程。零样本提示在所有模型家族和规模下性能接近随机水平。使用少于1%可训练权重的LoRA低秩适配，多数情况下可实现低于2%的集内ACER，但跨数据集ACER显著更高。研究指出LoRA主要优化集内决策边界，预训练表示和适配数据集对跨数据集泛化的影响大于轻量适配策略。
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 detectors from near-perfect to nearly random. Foundation models (FMs) have emerged as a promising alternative because typical PAD datasets, such as the MCIO benchmarks (MSU-MFSD, CASIA-FASD, Replay-Attack, and OULU-NPU), are small relative to the scale used for web-based pretraining. However, existing PAD systems primarily focus on CLIP-based foundation models, while overlooking other FMs with different architectures and training procedures. This study addresses this question by systematically evaluating 32 FMs. Zero-shot prompting achieves performance near chance across model families and scales. The vision encoders, when low-rankadapted (LoRA) with fewer than 1% trainable weights, achieve below 2% intra-dataset ACER in most cases, while cross-dataset ACER is substantially higher. LoRA primarily refines the decision boundary within a dataset, suggesting that pretrained representations and the adaptation dataset play a larger role in cross-dataset generalization than the evaluated lightweight adaptation strategy.