MAD-Sherlock: Multi-Agent Debates for Out-of-Context Misinformation Detection

Kumud Lakara, Juil Sock, Christian Rupprecht, Philip Torr, John Collomosse, Christian Schroeder de Witt

Keywords: misinformation detection, out-of-context image use, LLMs, multimodal models, multi-agent debates, safety

TL;DR: Multi-Agent Debates for Out-of-Context Misinformation Detection

Abstract: One of the most challenging forms of misinformation involves the out-of-context (OOC) use of images paired with misleading text, creating false narratives. Existing AI-driven detection systems lack explainability and require expensive finetuning. We address these issues with MAD-Sherlock: a Multi-Agent Debate system for OOC Misinformation Detection. MAD-Sherlock introduces a novel multi-agent debate framework where multimodal agents collaborate to assess contextual consistency and request external information to enhance cross-context reasoning and decision-making. Our framework enables explainable detection with state-of-the-art accuracy even without domain-specific fine-tuning. Extensive ablation studies confirm that external retrieval significantly improves detection accuracy, and user studies demonstrate that MAD-Sherlock boosts performance for both experts and non-experts. These results position MAD-Sherlock as a powerful tool for autonomous and citizen intelligence applications.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 7941