计算机科学与探索2026，Vol.20Issue(5)：1431-1442,12.DOI:10.3778/j.issn.1673-9418.2506029
融合语义聚类与Haar小波频域的多模态虚假新闻检测方法
Multimodal False News Detection Method Based on Semantic Clustering and Haar Wavelet Frequency Domain
摘要
Abstract
The existing multimodal fake news detection methods mostly analyze the text information from the global semantic perspective,ignoring the inconsistency between local semantics,resulting in insufficient fusion and low correlation between modalities.There are limitations in the capture and utilization of the multi-level frequency domain features of the image and the embedded text information,which makes the model difficult to mine the potential semantic features of the image.To solve the above problems,this paper proposes a multimodal fake news detection method based on semantic clustering and Haar wavelet frequency domain features(SC-HWFF-MFD).Firstly,a semantic representation space for multi-context differentiation is constructed,and an unsupervised context semantic alignment optimization stage is designed to enhance the semantic consistency between the local semantic features of news and the corresponding context.Secondly,the Haar wavelet transform is used to realize the hierarchical modeling of the high-frequency detail features(such as texture and edge)and the low-frequency background and structure information of the image.The multi-scale convolution and attention mechanism are introduced to realize the deep correlation and interaction between the spatial domain and frequency domain of visual features,and improve the representation ability of visual features.In addition,in order to solve the problem of mismatch between image and text features,an image-text matching fusion strategy is designed.The strategy uses the image text as auxiliary information and combines the attention fusion module to gradually realize the alignment and fusion of the image and text.Experimental results show that the classification accuracy Acc,F1 of fake news and F1 of real news of SC-HWFF-MFD method on Weibo dataset and Twitter dataset are 92.2%,91.8%,91.5%,84.9%,83.7%and 84.9%,respectively,which are better than the existing baseline models.The effectiveness of the proposed method in fake news identification is confirmed.关键词
多模态虚假新闻检测/语义聚类/Haar小波频域/图文匹配融合/注意力机制Key words
multimodal false news detection/semantic clustering/Haar wavelet frequency domain/image-text matching fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
杨力,廖远..融合语义聚类与Haar小波频域的多模态虚假新闻检测方法[J].计算机科学与探索,2026,20(5):1431-1442,12.基金项目
国家自然科学基金(61175122) （61175122）
四川省科技计划项目(2022NSFSC0555).This work was supported by the National Natural Science Foundation of China(61175122),and the Science and Technology Program of Sichuan Province(2022NSFSC0555). （2022NSFSC0555）
