Abstract: Fake news is usually spread by exaggerated,distorted or misleading statements,which can shape negative social opinion and seriously endanger public safety.The current fake news is usually multi-modal,and the existing detection methods often fuse the features of each modality after extracting them separately,ignoring the correlation between modalities,which makes it difficult to fully capture the details and correlation information,resulting in unsatisfactory detection performance.To solve these pro-blems,this paper proposes a detection model based on cross-modal feature fusion and alignment(CMFFA).CMFFA optimizes the modes of feature extraction,fusion and classification,extracts modal features from macro and micro perspectives,enhances features through the attention mechanism,and evaluates the ambiguity between modalities by calculating the similarity between modalities,so as to adaptively perform cross-modal feature fusion.Firstly,the pre-trained model is used to encode the single-modal features and cross-modal features of text and image.Then,in the cross-modal feature fusion,the proportion of cross-modal features is adaptively adjusted through the ambiguity analysis between modalities,so as to better realize the fusion of cross-modal features and improve the performance of fake news detection.Experimental results show that,compared with the existing fake news detection methods on the public Chinese and English fake news datasets,the proposed model significantly improves the F1 value,precision and recall rate,verifying its effectiveness and superiority.

Key words: Fake news detection, Cross-modal feature fusion, Inter-modal ambiguity analysis, Attention mechanism