SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection

2026-07-04

AI Summary

Existing deepfake face detection methods struggle to capture subtle inconsistencies among facial regions. To address this limitation, this work proposes a novel graph neural network framework that uniquely integrates semantic parsing with geometric landmarks to construct Semantics-Geometry Fusion (SGF) nodes. Building upon these nodes, the authors design a Consistency-Divergence Graph Neural Network (CD-GNN) that simultaneously models both the biological plausibility and structural conflicts among facial components through dual parallel pathways. Extensive experiments demonstrate that the proposed method significantly outperforms current state-of-the-art approaches across multiple public benchmarks, achieving notable improvements in both detection accuracy and robustness against diverse deepfake generation techniques.

Abstract

The rapid advancement of deepfakes necessitates robust face forgery detection. Although forged faces may lack obvious artifacts, they often contain subtle disharmony among different facial regions. We propose SGF-CDNet, a Consistency-Discrepancy Graph Network (CD-GNN) over Semantic-Geometric Fused (SGF) nodes. First, SGF-CDNet constructs SGF nodes by deeply fusing semantic regions from face parsing with geometric information from facial landmarks, allowing nodes to capture both high-level concepts and precise geometric constraints. Next, a dual-path CD-GNN performs parallel relational reasoning on these nodes across two dimensions: consistency and discrepancy. The consistency path evaluates if facial components follow natural biological patterns, while the discrepancy path mines for structural tensions and feature conflicts introduced by forgeries. By integrating these processes, our model effectively identifies disharmonious relationships between facial components. Extensive experiments on public datasets demonstrate that SGF-CDNet achieves superior performance, establishing it as a reliable solution for face forgery detection.

Problem

• face forgery detection
• deepfakes
• facial disharmony
• semantic-geometric fusion
• relational reasoning

Innovation

• Semantic-Geometric Fusion
• Consistency-Discrepancy Graph Network
• Face Forgery Detection
• Dual-path Relational Reasoning
• Facial Disharmony Modeling