融合图文特征的多模态谣言检测方法

doi:10.3969/j.issn.0255-8297.2026.03.011

Abstract

Abstract: To further improve the effectiveness and stability of rumor detection, a multimodal rumor detection method was proposed that integrated image and text features with adaptive noise resistance and semantic restoration. First, a three-stage processing pipeline consisting of pretrained encoding, dynamic pooling, and multi-head enhancement was designed to encode rumor-related texts and comments into semantic vectors. Then,two parallel modules were constructed: one for visual feature extraction and the other for optical character feature extraction. These modules encoded rumor images and explicit text within them into complementary enhanced image vectors. Finally, adaptive gating and dynamic cross-attention mechanisms were used to filter noise and enhance semantics,achieving local alignment and global integration of image and text information. Experimental results show that, compared with baseline algorithms, the proposed method can effectively capture deep correlations between image and text information and improve credibility and practicality of rumor detection results.

Key words: public security intelligence, rumor detection, image-text semantic matching, multimodal fusion

CLC Number:

GAO Guangliang, LIANG Weichao, ZHU Tao, HONG Lei, XIA Lingling. Multi-modal Rumor Detection Method Fusing Image and Text Features[J]. Journal of Applied Sciences, 2026, 44(3): 503-514.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2026.03.011

https://www.jas.shu.edu.cn/EN/Y2026/V44/I3/503

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Multi-modal Rumor Detection Method Fusing Image and Text Features

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