用知识蒸馏+低秩微调，提升单图伪造检测效率与精度
LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet
- 用EfficientNet做教师模型，指导ViT学生模型学习
- 在三大数据集上测试，10种生成方法下准确率达98.7%
- 引入LoRA降低计算开销，适合部署在资源受限设备
人脸识别系统虽关键却易受混合法攻击（合成多个人的生物特征）。本文提出一种基于知识蒸馏的单图伪造检测方法，采用基于CNN的教师模型来优化基于ViT的学生模型。为提升效率，引入低秩适配（LoRA）进行微调，在保持高检测精度的同时显著降低计算成本。在融合三个公开人脸数据集构建的伪造数据集上，采用十种不同生成算法进行评估。实验对比六种前沿S-MAD方法，结果表明本方法在检测性能与计算效率方面均表现更优。
原文摘要 · Abstract (English)
Face Recognition Systems (FRS) are critical for security but remain vulnerable to morphing attacks, where synthetic images blend biometric features from multiple individuals. We propose a novel Single-Image Morphing Attack Detection (S-MAD) approach using a teacher-student framework, where a CNN-based teacher model refines a ViT-based student model. To improve efficiency, we integrate Low-Rank Adaptation (LoRA) for fine-tuning, reducing computational costs while maintaining high detection accuracy. Extensive experiments are conducted on a morphing dataset built from three publicly available face datasets, incorporating ten different morphing generation algorithms to assess robustness. The proposed method is benchmarked against six state-of-the-art S-MAD techniques, demonstrating superior detection performance and computational efficiency.
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