用生成证据+大模型推理，让假新闻检测更准更可信
Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning
- 用扩散模型生成反驳或证实的多媒体证据
- 多智能体大模型链式推理提升判断准确率
- 适合需要可解释性的假新闻检测场景
虚假新闻在多模态平台上的快速传播严重威胁信息可信度。本文提出一种去伪与推断框架（DIFND），通过利用辟谣知识提升假新闻检测的性能与可解释性。DIFND结合条件扩散模型的生成能力与多模态大语言模型（MLLM）的协同推理能力。具体而言，采用辟谣扩散模型基于新闻视频的多模态内容生成反驳或证实的证据，丰富评估过程中的语义一致且多样化的合成样本。为增强推理能力，提出链式辟谣策略，由多智能体MLLM系统生成基于逻辑、具备多模态感知的推理内容及最终真伪判断。通过统一架构联合建模多模态特征、生成式辟谣线索与推理丰富的验证过程，DIFND显著提升检测准确率。在FakeSV和FVC数据集上的大量实验表明，该方法不仅优于现有方法，还能输出可信决策。
原文摘要 · Abstract (English)
The rapid spread of fake news across multimedia platforms presents serious challenges to information credibility. In this paper, we propose a Debunk-and-Infer framework for Fake News Detection(DIFND) that leverages debunking knowledge to enhance both the performance and interpretability of fake news detection. DIFND integrates the generative strength of conditional diffusion models with the collaborative reasoning capabilities of multimodal large language models (MLLMs). Specifically, debunk diffusion is employed to generate refuting or authenticating evidence based on the multimodal content of news videos, enriching the evaluation process with diverse yet semantically aligned synthetic samples. To improve inference, we propose a chain-of-debunk strategy where a multi-agent MLLM system produces logic-grounded, multimodal-aware reasoning content and final veracity judgment. By jointly modeling multimodal features, generative debunking cues, and reasoning-rich verification within a unified architecture, DIFND achieves notable improvements in detection accuracy. Extensive experiments on the FakeSV and FVC datasets show that DIFND not only outperforms existing approaches but also delivers trustworthy decisions.
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