Abstract
In recent years, the spread of fake news in social networks has become a serious threat to network security. To address this problem, various fake news detection methods have been proposed. However, most of the existing methods cannot jointly capture the intra-modal and inter-modal correlation relationships between image regions and text fragments, resulting in the model not making full use of multimodal information, thus limiting their ability to detect fake news accurately. To solve this limitation, we propose a novel fake news detection method based on a multimodal cooperative attention network (MCAND). Firstly, we use BERT and VGG19 to learn text and image representations, respectively. Secondly, the multimodal cooperative attention network is used to generate the high-order fusion features that fuse the image and text representations by calculating the similarity between the information segments in the modalities and the inter-modal similarity. Finally, the multimodal fusion features are input into the fake news detector to identify fake news. The experimental results show that the proposed MCAND has outperformed the state-of-the-art (SOTA) method in terms of performance, demonstrating its effectiveness.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 62201576), the Civil Aviation Joint Research Fund Project of the National Natural Science Foundation of China (Grant No. U1833107), the Fundamental Research Funds for the Central Universities (Grant No. 3122022050), the Open Fund of the Information Security Evaluation Center of Civil Aviation University of China (ISECCA-202202), and the Discipline Development Funds of Civil Aviation University of China.
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Yang, H., Zhang, J., Hu, Z., Zhang, L., Cheng, X. (2023). A Fake News Detection Method Based on a Multimodal Cooperative Attention Network. In: Wang, D., Yung, M., Liu, Z., Chen, X. (eds) Information and Communications Security. ICICS 2023. Lecture Notes in Computer Science, vol 14252. Springer, Singapore. https://doi.org/10.1007/978-981-99-7356-9_44
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DOI: https://doi.org/10.1007/978-981-99-7356-9_44
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