Knowledge-Enhanced Dynamic Scene Graph Attention Network for Fake News Video Detection

With the rapid rise of short video social platforms, the spread of fake news videos has become a global challenge. Short videos, which integrate multiple modalities such as text, images, and audio, have a powerful visual and auditory impact, making fake news more prone to widespread dissemination and causing serious societal consequences. However, the complex fusion of multimodal information in fake news videos, coupled with editing artifacts that often blur the distinction between real and fake content, presents considerable challenges to traditional detection methods. To address these challenges, this paper proposes a fake news video detection method based on the Knowledge-Enhanced Dynamic Scene Graph Attention Network (KDSGAT). This method captures temporal correlations and local semantic differences in visual scenes by leveraging dynamic scene graph networks, while enhancing semantic understanding through knowledge distillation from external knowledge graphs. Specifically, we first use pre-trained models such as BERT, HuBERT, and Swin Transformer to extract text semantic features, audio emotion features, and visual features, respectively. Next, we apply an unbiased scene graph generation approach to convert keyframes from the video into scene graphs, which are then processed by the dynamic scene graph attention network to capture temporal correlations and local semantic variations within the scene graph sequences. Finally, co-attention is used to interactively fuse multimodal features, enabling precise detection of fake news in videos. We conduct extensive experiments on two real-world datasets from short video social platforms, FakeSV and FakeTT. The results show that our method outperforms state-of-the-art baselines, improving accuracy by 1.86% and 2.68% on the two datasets, respectively. The source code and data are available at https://github.com/xuejianhuang/KDSGAT-FNVD.