Multimodal Fact Checking with Unified Visual, Textual, and Contextual Representations

2025-08-07

AI Summary

Traditional text-based fact-checking systems struggle to effectively verify multimodal misinformation involving both text and images. To address this, we propose MultiCheck, a fine-grained multimodal fact-checking framework that jointly models textual, visual, and contextual representations. MultiCheck explicitly captures cross-modal semantic alignments through an element-level cross-modal interaction mechanism and a contrastive learning objective, thereby enhancing both interpretability and generalization. The architecture comprises dedicated text and vision encoders, a cross-modal fusion module, and a classification head, with contrastive learning employed to optimize semantic alignment across modalities. Evaluated on the Factify-2 benchmark, MultiCheck achieves a weighted F1 score of 0.84—significantly outperforming existing baselines. This work provides an interpretable and robust solution for multimodal fact-checking, advancing the state of the art in verifiable multimodal reasoning.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The growing rate of multimodal misinformation, where claims are supported by both text and images, poses significant challenges to fact-checking systems that rely primarily on textual evidence. In this work, we have proposed a unified framework for fine-grained multimodal fact verification called "MultiCheck", designed to reason over structured textual and visual signals. Our architecture combines dedicated encoders for text and images with a fusion module that captures cross-modal relationships using element-wise interactions. A classification head then predicts the veracity of a claim, supported by a contrastive learning objective that encourages semantic alignment between claim-evidence pairs in a shared latent space. We evaluate our approach on the Factify 2 dataset, achieving a weighted F1 score of 0.84, substantially outperforming the baseline. These results highlight the effectiveness of explicit multimodal reasoning and demonstrate the potential of our approach for scalable and interpretable fact-checking in complex, real-world scenarios.

Problem

• Detecting multimodal misinformation combining text and images
• Improving fact-checking accuracy with unified visual-textual analysis
• Enhancing cross-modal reasoning for scalable fact verification

Innovation

• Unified framework for multimodal fact verification
• Fusion module captures cross-modal relationships
• Contrastive learning aligns claim-evidence pairs