MIRAGE: Agentic Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning

• URL: http://arxiv.org/abs/2510.17590v1
• Date: Mon, 20 Oct 2025 14:40:26 GMT
• Title: MIRAGE: Agentic Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning
• Authors: Mir Nafis Sharear Shopnil, Sharad Duwal, Abhishek Tyagi, Adiba Mahbub Proma,
• Abstract summary: We present MIRAGE, an inference-time, model-pluggable agentic framework that decomposes multimodal verification into four sequential modules.<n> visual veracity assessment detects AI-generated images, cross-modal consistency analysis identifies out-of-context repurposing, retrieval-augmented factual checking grounds claims in web evidence.<n>MIRAGE orchestrates vision-language model reasoning with targeted web retrieval, outputs structured and citation-linked rationales.
• Score: 0.6475163438744868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Misinformation spreads across web platforms through billions of daily multimodal posts that combine text and images, overwhelming manual fact-checking capacity. Supervised detection models require domain-specific training data and fail to generalize across diverse manipulation tactics. We present MIRAGE, an inference-time, model-pluggable agentic framework that decomposes multimodal verification into four sequential modules: visual veracity assessment detects AI-generated images, cross-modal consistency analysis identifies out-of-context repurposing, retrieval-augmented factual checking grounds claims in web evidence through iterative question generation, and a calibrated judgment module integrates all signals. MIRAGE orchestrates vision-language model reasoning with targeted web retrieval, outputs structured and citation-linked rationales. On MMFakeBench validation set (1,000 samples), MIRAGE with GPT-4o-mini achieves 81.65% F1 and 75.1% accuracy, outperforming the strongest zero-shot baseline (GPT-4V with MMD-Agent at 74.0% F1) by 7.65 points while maintaining 34.3% false positive rate versus 97.3% for a judge-only baseline. Test set results (5,000 samples) confirm generalization with 81.44% F1 and 75.08% accuracy. Ablation studies show visual verification contributes 5.18 F1 points and retrieval-augmented reasoning contributes 2.97 points. Our results demonstrate that decomposed agentic reasoning with web retrieval can match supervised detector performance without domain-specific training, enabling misinformation detection across modalities where labeled data remains scarce.

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