计算机科学与探索2026，Vol.20Issue(1)：194-205,12.DOI:10.3778/j.issn.1673-9418.2501008
注意力引导多模态特征融合的虚假新闻检测方法
Fake News Detection Method Based on Attention-Guided Multimodal Feature Fusion
摘要
Abstract
Existing multimodal fake news detection methods have limitations in utilizing image's multilayered frequency-domain information and enabling deep interactions between modalities,making it difficult to fully exploit the latent fea-tures of images and the correlations between multimodal features,which in turn affects detection performance.Fake news images typically undergo multiple compression or tampering operations during their dissemination,which leads to abnormal frequency-domain responses.Traditional methods often rely on Fourier transforms to extract frequency-domain features.However,their global frequency analysis may miss local tampering traces and thus fail to achieve multi-scale feature decoupling.To better uncover and fully utilize these critical features and their inherent relationships,thereby improving fake news detection performance,an attention-guided multimodal feature fusion method for fake news detection(AGMFN)is proposed.This method models the multi-layered frequency-domain information of images using a wavelet-transform-based dual-path feature extraction module.It captures low-frequency global structures and high-frequency local anomalies through two-level wavelet decomposition,while enhancing detail features through feature-boosting convolution.Meanwhile,pre-trained models and frequency-domain feature extraction modules are employed to separately extract textual,visual,and frequency-domain features,constructing a joint framework for physical forensics and semantic clues.To enable multi-modal feature fusion and capture deep correlations between different modalities,an attention mechanism-based long-sequence feature fusion module is designed,introducing an exponentially decaying weighting coefficient to model long-term dependencies between modalities and solve the temporal mismatch issue in traditional concatenation-based fusion.Through cross-modal attention,a hierarchical fusion of text,frequency-domain,and visual features is achieved,enhancing fake news detection capability while maintaining computational efficiency.Experimental results show that AGMFN achieves classification accuracies of 0.917 and 0.847 on the Weibo and Twitter datasets,respectively,outperforming exist-ing baseline models.Visualization experiments further confirm that the fused multimodal features exhibit stronger general-ization ability,thus improving the performance of fake news detection.关键词
虚假新闻检测/小波变换/注意力机制/多模态特征融合Key words
fake news detection/wavelet transform/attention mechanism/multimodal feature fusion分类
信息技术与安全科学引用本文复制引用
邓兴宇,王龙业,曾晓莉,叶浩,车熹昊..注意力引导多模态特征融合的虚假新闻检测方法[J].计算机科学与探索,2026,20(1):194-205,12.基金项目
国家自然科学基金(62161047).This work was supported by the National Natural Science Foundation of China(62161047). （62161047）
