Abstract: In recent years,with the rapid development of the Internet and multimedia technology,it is more convenient for people to obtain information,but the spread of fake information on the Internet is also increasingly serious,and the negative impact is constantly expanding.In order to enhance the credibility and deception,fake information presents a multi-modal development trend,which makes the detection work face greater challenges.The existing multi-modal fake information detection methods pay more attention to the formation of multi-modal features.The research on the contribution rate of cross-modal ambiguity and different modal features in detection is not perfect,ignoring the impact of inherent differences among different modal features on fake information detection.To solve the problem,this paper proposes to construct an improved fake information detection model based on cross-modal correlation ambiguity learning.Through cross-modal ambiguity learning of text and image features,the weights of unimodal features and fused features are updated by the ambiguity score.The unimodal features and fused features are combined adaptively,and the weights of text and image features are dynamically assigned by grid search to improve the detection accuracy.The effectiveness of the model is verified by experiments on the Twitter dataset.The accuracy is improved by 6% compared with the baseline model and 1.6% compared with the detection without dynamic weight assignment.

Key words: Fake news detection, Multimodal, Cross-modal correlation, Ambiguity learning, Fusion features