The rapid advancement of social media platforms has significantly reduced the cost of information dissemination, yet it has also led to a proliferation of fake news, posing a threat to societal trust and credibility. Most of fake news detection research focused on integrating text and image information to represent the consistency of multiple modes in news content, while paying less attention to inconsistent information. Besides, existing methods that leveraged inconsistent information often caused one mode overshadowing another, leading to ineffective use of inconsistent clue. To address these issues, we propose an adaptive multi-modal feature fusion network (MFF-Net). Inspired by human judgment processes for determining truth and falsity in news, MFF-Net focuses on inconsistent parts when news content is generally consistent and consistent parts when it is generally inconsistent. Specifically, MFF-Net extracts semantic and global features from images and texts respectively, and learns consistency information between modes through a multiple feature fusion module. To deal with the problem of modal information being easily masked, we design a single modal feature filtering strategy to capture inconsistent information from corresponding modes separately. Finally, similarity scores are calculated based on global features with adaptive adjustments made to achieve weighted fusion of consistent and inconsistent features. Extensive experimental results demonstrate that MFF-Net outperforms state-of-the-art methods across three public news datasets derived from real social medias.

本研究解决了假新闻检测中对不一致信息关注不足的问题，提出了一种适应性多模态特征融合网络（MFF-Net）。该方法专注于当新闻内容一致时的不一致部分以及不一致时的一致部分，从而有效利用不同模态的信息，实验结果表明MFF-Net在假新闻检测方面优于现有的最先进方法。

适应性学习一致性与不一致性信息以检测假新闻