# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 1 Introduction  
As an important way of information dissemination, network news covers a wide range of fields and has rich and varied contents. It can meet the spiritual and cultural needs of different groups and improve the quality of public spiritual life. Network news is characterized by fast dissemination speed and strong timeliness. It can make it easier for the public to obtain information and participate in the discussion of social hotspots. With the rise of the Internet, especially social media platforms (e.g., Facebook, Twitter, WeChat, Weibo, etc. ), the speed and scope of information dissemination have greatly expanded. This makes it possible for any piece of news, opinion, or rumor to quickly spread across the globe. However, information on social media is often not rigorously vetted or verified, and the spread of fake news has become much easier. Such false information not only misleads public perception and affects individual decisions and behaviors, but may also undermine trust between the government, the media and the public, causing social instability. Given the double-edged sword effect of online news dissemination, the importance of fake news detection is becoming more and more significant. Traditional news verification relies on the verification process of journalists and media organizations. However, with the explosive growth of information, the limitations of manual verification in terms of speed and scale are becoming increasingly apparent. In addition, the anonymity and decentralized nature of social media makes it more difficult to track and verify sources. With the rapid development of AI, NLP, ML and Big Data, fake news detection is able to automatically identify and filter fake news by using technical means to analyze news text and images. On this basis, combined with a professional news review team, it is able to effectively identify and intercept fake news and reduce its negative impact on the public and society. Meanwhile, fake news detection techniques continue to improve. For example, the rise of large-scale language modeling and multimodal fusion technology has made it possible to detect fake news from multiple dimensions such as text and image. Currently, the research on fake news detection has mainly experienced two stages: unimodal and multimodal. Early research is mostly in the unimodal stage, and its research object is relatively single, dominated by text or images. There are problems such as limited information and fuzzy semantics. The core task of multimodal false news detection lies in the feature extraction and feature fusion of news information. Therefore, how to effectively extract features and how to integrate multimodal data features such as text and images have become the hotspots and difficulties of current research. In the actual scenario of fast spreading of fake news, there is often a lag in obtaining external evidence for detection. And the reliability and completeness of the evidence sources are difficult to guarantee, which may introduce additional noise and interfere with the model judgment. In addition, the semantic inconsistency problem of multimodal features can lead to feature fusion difficulties. Traditional single fusion methods (e.g., simple splicing and weighting of text and image features directly) are prone to information loss due to the semantic gap between text and visual features. If the fused features after single fusion are used directly for detection, there will be the problem of under-utilization of unimodal text and image features. For this reason, this paper focuses on mining the text and image information of the news itself to improve the detection ability of the model when it is not supported by external evidence. A Cross-modal False News Detection Model Based on Multi-level Fusion without Evidence (CM-MLF) is proposed. The model relies solely on the text and image content of the news for detection. This makes it especially suitable for real-time screening of news in content review systems on social media platforms.