LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet
This addresses security vulnerabilities in face recognition systems against morphing attacks, but it is incremental as it builds on existing teacher-student and LoRA techniques.
The paper tackled the problem of detecting morphing attacks in face recognition systems by proposing a single-image approach using a teacher-student framework with LoRA-enhanced ViT, achieving superior detection performance and computational efficiency compared to six state-of-the-art methods.
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.