LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet

• URL: http://arxiv.org/abs/2511.12602v1
• Date: Sun, 16 Nov 2025 13:58:11 GMT
• Title: LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet
• Authors: Ria Shekhawat, Sushrut Patwardhan, Raghavendra Ramachandra, Praveen Kumar Chandaliya, Kishor P. Upla,
• Abstract summary: We propose a novel Single-Image Morphing Attack Detection (S-MAD) approach using a teacher-student framework.<n>To improve efficiency, we integrate Low-Rank Adaptation (LoRA) for fine-tuning, reducing computational costs while maintaining high detection accuracy.<n>The proposed method is benchmarked against six state-of-the-art S-MAD techniques, demonstrating superior detection performance and computational efficiency.
• Score: 7.409512128477373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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