MultiCheck: Strengthening Web Trust with Unified Multimodal Fact Verification

Misinformation on the web increasingly appears in multimodal forms, combining text, images, and OCR-rendered content in ways that amplify harm to public trust and vulnerable communities. While prior fact-checking systems often rely on unimodal signals or shallow fusion strategies, modern misinformation campaigns operate across modalities and require models that can reason over subtle cross-modal inconsistencies in a transparent and responsible manner. We introduce MultiCheck, a lightweight and interpretable framework for multimodal fact verification that jointly analyzes textual, visual, and OCR evidence. At its core, MultiCheck employs a relational fusion module based on element-wise difference and product operations, allowing for explicit cross-modal interaction modeling with minimal computational overhead. A contrastive alignment objective further helps the model distinguish between supporting and refuting evidence while maintaining a small memory and energy footprint, making it suitable for low-resource deployment. Evaluated on the Factify-2 (5-class) and MOCHEG (3-class) benchmarks, MultiCheck achieves substantial performance improvement and remains robust under noisy OCR and missing modality conditions. Overall, MultiCheck is efficient, interpretable, and robust for multimodal verification. Our code is available at the following: https://github.com/Adityakishore09/MultiCheck_WWW-2026 GitHub repository.