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Open Access

ARTICLE

Fake News Detection Based on Cross-Modal Ambiguity Computation and Multi-Scale Feature Fusion

Jianxiang Cao1, Jinyang Wu1, Wenqian Shang1,*, Chunhua Wang1, Kang Song1, Tong Yi2,*, Jiajun Cai1, Haibin Zhu3
1 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
2 School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China
3 Department of Computer Science, Nipissing University, North Bay, ON P1B 8L7, Canada
* Corresponding Author: Wenqian Shang. Email: email; Tong Yi. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.060025

Received 22 October 2024; Accepted 21 February 2025; Published online 19 March 2025

Abstract

With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the textual content, image content, and frequency domain features. Then, it employs a Co-Attention mechanism for cross-modal fusion. Additionally, a semantic consistency detection module is designed to quantify semantic deviations, thereby enhancing the performance of fake news detection. Experimentally verified on two commonly used datasets, Twitter and Weibo, the model achieved F1 scores of 90.0% and 94.0%, respectively, significantly outperforming the pre-modified MFFFND (Multimodal Feature Fusion Fake News Detection with Attention Block) model and surpassing other baseline models. This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.

Keywords

Fake news detection; multimodal; cross-modal ambiguity computation; multi-scale feature fusion

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