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

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 features 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 can overlook local tampering traces and 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 multimodal 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 existing baseline models. Visualization experiments further confirm that the fused multimodal features exhibit stronger generalization ability, thus improving the performance of fake news detection.

Key words: fake news detection, wavelet transform, attention mechanism, multimodal feature fusion