Entity-Aware Cross-Modal Fusion Network for Fine-Grained Entity Consistency Verification in Multimodal News Misinformation Detection

Multimodal misinformation demands robust Cross-modal Entity Consistency (CEC) verification, aligning textual entities with visual depictions. Current large vision-language models (LVLMs) struggle with fine-grained entity verification, especially in complex "contextual mismatch" scenarios, failing to capture intricate relationships or leverage auxiliary information. To address this, we propose the Entity-Aware Cross-Modal Fusion Network (EACFN), a novel architecture for deep semantic alignment and robust integration of external visual evidence. EACFN incorporates modules for entity encoding, cross-attention for reference image enhancement, and a Graph Neural Network (GNN)-based module for explicit inter-modal relational reasoning, culminating in fine-grained consistency predictions. Experiments on three annotated datasets demonstrate EACFN's superior performance, significantly outperforming state-of-the-art zero-shot LVLMs across tasks, particularly with reference images. EACFN also shows improved computational efficiency and stronger agreement with human judgments in ambiguous contexts. Our contributions include the innovative EACFN architecture, its GNN-based relational reasoning module, and effective integration of reference image information for enhanced verification robustness.