信息安全研究2025，Vol.11Issue(8)：693-701,9.DOI:10.12379/j.issn.2096-1057.2025.08.02
基于跨模态注意力机制和弱监督式对比学习的虚假新闻检测模型
Fake News Detection Model Based on Cross-modal Attention Mechanism and Weak-supervised Contrastive Learning
摘要
Abstract
With the widespread popularization of the Internet and smart devices,social media has become a major platform for news dissemination.However,it also provides conditions for the widespread of fake news.In the current social media environment,fake news exists in multiple modalities such as text and images,while existing multimodal fake news detection techniques usually fail to fully explore the intrinsic connection between different modalities,which limits the overall performance of the detection model.To address this issue,this paper proposes a hybrid model of cross-modal attention mechanism and weak-supervised contrastive learning(CMAWSCL)for fake news detection.The model utilizes pre-trained BERT and ViT models to extract text and image features respectively,and effectively fuses multimodal features through the cross-modal attention mechanism.At the same time,the model introduces weak-supervised contrast learning,which utilizes the prediction results of effective modalities as supervisory signals to guide the contrast learning process.This approach can effectively capture and utilize the complementary information between text and image,thus enhancing the performance and robustness of the model in multimodal environments.Simulation experiments show that the CMAWSCL performs well on the publicly available Weibo17 and Weibo21 datasets,with an average improvement of 1.17 percentage points in accuracy and 1.66 percentage points in F1 score compared to the current state-of-the-art methods,which verifies its effectiveness and feasibility in coping with the task of multimodal fake news detection.关键词
虚假新闻检测/多模态融合/跨模态注意力机制/对比学习/深度学习Key words
fake news detection/multi-modal fusion/cross-modal attention mechanism/contrastive learning/deep learning分类
信息技术与安全科学引用本文复制引用
蔡松睿,张仕斌,丁润宇,卢嘉中,黄源源..基于跨模态注意力机制和弱监督式对比学习的虚假新闻检测模型[J].信息安全研究,2025,11(8):693-701,9.基金项目
国家重点研发计划项目(2022YFB3103103) （2022YFB3103103）
成都市重点研发项目(2023-XT00-00002-GX) （2023-XT00-00002-GX）
成都市重点研发支撑计划项目(2024-YF05-01227-SN) （2024-YF05-01227-SN）
