TruthDebate: Multi-Agent Collaborative Reasoning for Deepfake Detection

While multimodal large language models (MLLMs) have made significant progress for deepfake detection, manipulation of deepfake often exhibits various semantically coherent yet deceptive content such as face swap, face emotion editing or text fabrication, making single-view analysis of one MLLM insufficient. LLM-based multi-agent systems (MAS) provide a promising way that collectively mine deceptive content from complementary perspectives to combat this threat. However, conventionally MAS suffer from high computational costs and significant optimization difficulties. To solve these challenges, we propose an adaptive multi-agent framework named TruthDebate for deepfake detection. TruthDebate consists of two core stages: (1) we design a multimodal clue extraction model to capture visual and textual clues of deceptive content to provide prior knowledge as the input of the MAS; (2) Guided by these clues, we design an efficiency-aware multi-agent collaborative reasoning mechanism that includes multimodal clue analysts, evidence integrators, an evidence auditor, and a decision-maker for cooperative reasoning to adaptively optimize MAS topology and routing-based inference. Extensive experiments on the MLLM-Driven synthetic multimodal deepfake dataset demonstrate the effectiveness of our framework. Our framework significantly outperforms multimodal detectors and multi-agent methods while reducing token consumption. Code is available at https://anonymous.4open.science/r/TruthDebate-3131/.