Denoising-Enhanced Multimodal Detection Network for Fake News Video Detection

The proliferation of fake news on short video platforms has recently attracted widespread attention, prompting a growing interest in fake news video detection. While existing studies show initial success, they may be constrained by the following limitations: (1) Current detection approaches lack logical understanding and integration capabilities, thereby leading to models missing implicit essential news information. (2) They simultaneously exploit news content for detection but fail to effectively distinguish between informative and non-informative elements, causing the attention bias within the detection model. To address these limitations, we propose a Denoising-Enhanced Multimodal Detection Network (D2et) to mine implicit essential news information and filter out redundant information. Firstly, to improve comprehension of essential content, we leverage the large language model to mine tags from two levels: (1) Global-Insight Tag, which captures the overall content of the short video; and (2) Local-Veracity Tag, which identifies essential details. We obtain the global and local level tags to jointly extract the explicit and structured content for clarifying essential information. Secondly, we design an information denoising mechanism to eliminate redundant noise. This mechanism refines multimodal features guided by the extracted tags, ensuring that the model focuses on relevant content and suppresses redundant information, thereby improving detection performance. Extensive experiments on two datasets demonstrate the effectiveness of our method, validating the contributions of multi-level tags and the information denoising mechanism.