The easy sharing of multimedia content on social media has caused a rapid
dissemination of fake news, which threatens society's stability and security.
Therefore, fake news detection has garnered extensive research interest in the
field of social forensics. Current methods primarily concentrate on the
integration of textual and visual features but fail to effectively exploit
multi-modal information at both fine-grained and coarse-grained levels.
Furthermore, they suffer from an ambiguity problem due to a lack of correlation
between modalities or a contradiction between the decisions made by each
modality. To overcome these challenges, we present a Multi-grained Multi-modal
Fusion Network (MMFN) for fake news detection. Inspired by the multi-grained
process of human assessment of news authenticity, we respectively employ two
Transformer-based pre-trained models to encode token-level features from text
and images. The multi-modal module fuses fine-grained features, taking into
account coarse-grained features encoded by the CLIP encoder. To address the
ambiguity problem, we design uni-modal branches with similarity-based weighting
to adaptively adjust the use of multi-modal features. Experimental results
demonstrate that the proposed framework outperforms state-of-the-art methods on
three prevalent datasets.

文章介绍了一种用于检测假新闻的多粒度多模态融合网络模型，并将模型性能与现有方法进行了比较。