MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning
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| Format: | Preprint |
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2025
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| _version_ | 1866914507199086592 |
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| author | Shopnil, Mir Nafis Sharear Duwal, Sharad Tyagi, Abhishek Proma, Adiba Mahbub |
| author_facet | Shopnil, Mir Nafis Sharear Duwal, Sharad Tyagi, Abhishek Proma, Adiba Mahbub |
| contents | We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized modules: visual forensics, cross-modal alignment, retrieval-augmented claim verification, and calibrated judgment. On MMFakeBench, MERIT with GPT-4o-mini achieves 81.65% F1, outperforming all reported zero-shot baselines including GPT-4V with MMD-Agent (74.0% F1). A controlled same-model evaluation confirms gains stem from architectural design: MERIT achieves 6.14 points higher misinformation recall than MMD-Agent under identical model conditions, with per-class gains of +18.0 on visual distortion and +5.33 on textual distortion. Ablation studies reveal non-overlapping module specialization, where removing any module disproportionately degrades its target category while leaving others intact. Test set evaluation on 5,000 samples confirms generalization within 0.21 F1 points of validation results. The framework operates with any instruction-following vision-language model and produces citation-linked rationales for human review. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_17590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning Shopnil, Mir Nafis Sharear Duwal, Sharad Tyagi, Abhishek Proma, Adiba Mahbub Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Computers and Society Machine Learning I.2.7; H.3.3; I.4.9 We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized modules: visual forensics, cross-modal alignment, retrieval-augmented claim verification, and calibrated judgment. On MMFakeBench, MERIT with GPT-4o-mini achieves 81.65% F1, outperforming all reported zero-shot baselines including GPT-4V with MMD-Agent (74.0% F1). A controlled same-model evaluation confirms gains stem from architectural design: MERIT achieves 6.14 points higher misinformation recall than MMD-Agent under identical model conditions, with per-class gains of +18.0 on visual distortion and +5.33 on textual distortion. Ablation studies reveal non-overlapping module specialization, where removing any module disproportionately degrades its target category while leaving others intact. Test set evaluation on 5,000 samples confirms generalization within 0.21 F1 points of validation results. The framework operates with any instruction-following vision-language model and produces citation-linked rationales for human review. |
| title | MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Computers and Society Machine Learning I.2.7; H.3.3; I.4.9 |
| url | https://arxiv.org/abs/2510.17590 |