计算机应用研究2026，Vol.43Issue(9)：2597-2604,8.DOI:10.19734/j.issn.1001-3695.2026.01.0005
基于对抗鲁棒增强的多模态谣言检测方法
Adversarial robustness enhanced multimodal rumor detection method
摘要
Abstract
To address the limitations of existing rumor detection datasets being unimodal and the weak robustness of rumor de-tection models against multimodal attacks,this paper constructed a large-scale multimodal rumor dataset,MuRumor.This dataset contained 19 733 posts,658 557 comments,and 12 365 users,covering text,image,comment,user,and social con-text features.Subsequently,it proposed an adversarial robustness-enhanced multimodal rumor detection(AR-MRD)model.The model introduced text style weakening,comment filtering,and image adversarial training mechanisms,combined with a comment-assisted cross-modal consistency mechanism,achieving efficient fusion of multimodal features and significantly impro-ving the model's resistance to cross-modal adversarial attacks.Experimental results show that AR-MRD achieves an accuracy of 92.4％,surpassing existing detection methods,and maintains the lowest attack success rate under adversarial conditions,indicating its effectiveness in defending against tampering attacks on text,image,and comment modalities.关键词
社交媒体谣言/多模态谣言检测/对抗攻击/跨模态融合/鲁棒性Key words
social media rumor/multimodal rumor detection/adversarial attack/cross-modal fusion/robustness分类
信息技术与安全科学引用本文复制引用
李婧,陈欣雨,唐瑞,王文贤,王海舟..基于对抗鲁棒增强的多模态谣言检测方法[J].计算机应用研究,2026,43(9):2597-2604,8.基金项目
国家重点研发计划资助项目(2022YFC3303101) （2022YFC3303101）
中央高校基本科研业务费专项资金资助项目(SCU2024D012) （SCU2024D012）
