MEGRA: Mixture-of-Experts and Graph Retrieval-Augmented Generation for Explainable Face Forgery Detection

Conventional deepfake detection methods typically output only authenticity labels, forgery scores, or visual heatmaps, making it difficult to provide natural-language explanations that are readily understandable to humans. Recent methods based on multimodal large language models (MLLMs) can generate natural-language explanations, offering a new direction for explainable deepfake detection. However, they still face two challenges. First, their modeling of fine-grained forgery cues remains insufficient, resulting in poor generalization to forgery mechanisms unseen during training. Second, MLLM-generated explanations are prone to hallucination. To mitigate these issues, we propose MEGRA (Mixture-of-Experts and Graph Retrieval-Augmented Generation), a face forgery detection method that combines mechanism-aware Mixture-of-Experts (MoE) detection with Graph Retrieval-Augmented Generation (GraphRAG) explanation. MEGRA comprises two modules: a forgery detection module and an explanation module. In the forgery detection module, we design a mechanism-oriented Forgery Detection MoE (FD-MoE) that adaptively combines experts with complementary forensic capabilities to better discriminate among different forgery mechanisms and adapt to distribution shifts. In the explanation module, we construct face-Forensic Knowledge Graphs (F-FKGs) and introduce GraphRAG to retrieve relevant forensic knowledge conditioned on the detection result and regional forensic evidence, guiding the MLLM to generate evidence-grounded natural-language explanations. Experiments on HydraFake show that MEGRA achieves an average accuracy of 91.09%, reaching state-of-the-art (SOTA) performance. In addition, MEGRA’s explanations receive the highest scores among reported results under all three judge models: GPT-5.5, Kimi-K3, and Qwen3.7-Plus.