关键词: 人脸伪造检测, 多分支网络, Vision Transformer (ViT), 可逆神经网络, 边缘特征

Abstract: To address the problems of insufficient feature representation, low robustness, and weak generalization in handling diverse forgery types and low-quality images, a face forgery detection method based on tri-branch feature extraction (Tri-Branch Feature Extraction Network, Tri-BranchNet) was proposed to enhance forgery trace representation and detection performance. The architecture was designed with three branches: 1) global semantics were captured by using Vision Transformer (ViT); 2) local texture modeling was improved by Invertible Neural Network (INN); 3) an edge-aware branch was constructed to extract boundary-level features. Experimental results show that the proposed method achieves 98.75% accuracy on FaceForensics++ (C23), outperforming F3-Net (Frequency in Face Forgery Network) and CORE (COnsistent REpresentation learning) by 1.23% and 1.14%, respectively. In cross-compression and cross-dataset evaluations, AUC (Area Under Curve) scores reach 85.26% and 81.09% on C40 and Celeb-DF, demonstrating strong robustness and generalization. The proposed tri-branch fusion mechanism significantly enhances detection accuracy and provides a novel approach to multi-dimensional feature modeling.

Key words: face forgery detection, multi-branch network, Vision Transformer (ViT), invertible neural network, edge feature