提出多粒度约束网络，提升真假新闻的特征区分能力。
RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection
- 设计残差感知模块，在多尺度融合特征时保持一致性与独特性。
- 通过跨模态对与整体新闻分布约束，放大真伪差异。
- 适合关注多模态信息融合与虚假内容检测的研究者。
多模态假新闻检测旨在自动识别新闻的真实性，以减轻虚假信息带来的负面影响。尽管现有方法已证明有效，但在跨模态特征融合与优化方面仍存在挑战。为此，本文提出残差感知补偿网络与多粒度约束机制（RaCMC），旨在充分交互与融合跨模态特征，同时增强真实与虚假新闻之间的差异性。首先，设计多尺度残差感知补偿模块，在不同尺度上交互并融合特征，确保特征交互的一致性与排他性，从而获得高质量特征。其次，引入多粒度约束模块，限制新闻整体及图文对的分布，从而在新闻层级和特征层级放大真实与虚假新闻的差异。最后，构建主导特征融合推理模块，从一致性和不一致性双重角度综合评估新闻真实性。在Weibo17、Politifact和GossipCop三个公开数据集上的实验表明，所提方法具有显著优势。
原文摘要 · Abstract (English)
Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method.
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