In multimodal misinformation, deception usually arises not just from pixel-level manipulations in an image, but from the semantic and contextual claim jointly expressed by the image-text pair. Yet most deepfake detectors, engineered to detect pixel-level forgeries, do not account for claim-level meaning, despite their growing integration in automated fact-checking (AFC) pipelines. This raises a central scientific and practical question: Do pixel-level detectors contribute useful signal for verifying image-text claims, or do they instead introduce misleading authenticity priors that undermine evidence-based reasoning? We provide the first systematic analysis of deepfake detectors in the context of multimodal misinformation detection. Using two complementary benchmarks, MMFakeBench and DGM4, we evaluate: (1) state-of-the-art image-only deepfake detectors, (2) an evidence-driven fact-checking system that performs tool-guided retrieval via Monte Carlo Tree Search (MCTS) and engages in deliberative inference through Multi-Agent Debate (MAD), and (3) a hybrid fact-checking system that injects detector outputs as auxiliary evidence. Results across both benchmark datasets show that deepfake detectors offer limited standalone value, achieving F1 scores in the range of 0.26-0.53 on MMFakeBench and 0.33-0.49 on DGM4, and that incorporating their predictions into fact-checking pipelines consistently reduces performance by 0.04-0.08 F1 due to non-causal authenticity assumptions. In contrast, the evidence-centric fact-checking system achieves the highest performance, reaching F1 scores of approximately 0.81 on MMFakeBench and 0.55 on DGM4. Overall, our findings demonstrate that multimodal claim verification is driven primarily by semantic understanding and external evidence, and that pixel-level artifact signals do not reliably enhance reasoning over real-world image-text misinformation.

翻译：在多模态虚假信息中，欺骗性通常不仅源于图像中的像素级篡改，更源于图文对所共同表达的语义和上下文主张。然而，尽管深度伪造检测器越来越多地被集成到自动化事实核查（AFC）流程中，但大多数为检测像素级伪造而设计的检测器并未考虑主张层面的含义。这引发了一个核心的科学和实际问题：像素级检测器是否为验证图文主张提供了有用的信号，还是反而引入了误导性的真实性先验，从而破坏了基于证据的推理？我们首次对深度伪造检测器在多模态虚假信息检测背景下的作用进行了系统分析。使用两个互补的基准测试集MMFakeBench和DGM4，我们评估了：（1）最先进的纯图像深度伪造检测器，（2）一个证据驱动的事实核查系统，该系统通过蒙特卡洛树搜索（MCTS）执行工具引导的检索，并通过多智能体辩论（MAD）进行审慎推理，以及（3）一个将检测器输出作为辅助证据注入的混合事实核查系统。两个基准数据集的结果表明，深度伪造检测器提供的独立价值有限，在MMFakeBench上的F1分数在0.26-0.53之间，在DGM4上为0.33-0.49，并且由于非因果的真实性假设，将其预测纳入事实核查流程会持续导致性能下降0.04-0.08 F1。相比之下，以证据为中心的事实核查系统实现了最高性能，在MMFakeBench上的F1分数达到约0.81，在DGM4上达到0.55。总体而言，我们的研究结果表明，多模态主张验证主要由语义理解和外部证据驱动，而像素级伪影信号并不能可靠地增强对现实世界图文虚假信息的推理能力。