Evidence-Grounded Multimodal Misinformation Detection with Attention-Based GNNs

TL;DR

This paper introduces a graph neural network-based framework for detecting multimodal misinformation by assessing the consistency between images and captions using evidence and claim graphs, outperforming large language models.

Contribution

The work presents a novel graph-based approach that explicitly models and compares evidence and claim graphs for misinformation detection, addressing limitations of LLMs in contextual understanding.

Findings

Achieves 93.05% detection accuracy on the dataset.

Outperforms LLM-based methods by 2.82%.

Demonstrates effectiveness of task-specific graph neural networks.

Abstract

Multimodal out-of-context (OOC) misinformation is misinformation that repurposes real images with unrelated or misleading captions. Detecting such misinformation is challenging because it requires resolving the context of the claim before checking for misinformation. Many current methods, including LLMs and LVLMs, do not perform this contextualization step. LLMs hallucinate in absence of context or parametric knowledge. In this work, we propose a graph-based method that evaluates the consistency between the image and the caption by constructing two graph representations: an evidence graph, derived from online textual evidence, and a claim graph, from the claim in the caption. Using graph neural networks (GNNs) to encode and compare these representations, our framework then evaluates the truthfulness of image-caption pairs. We create datasets for our graph-based method, evaluate and compare our baseline model against popular LLMs on the misinformation detection task. Our method scores $93.05\%$ detection accuracy on the evaluation set and outperforms the second-best performing method (an LLM) by $2.82\%$, making a case for smaller and task-specific methods.