IDO: 面向多模态假新闻检测的不一致性感知分布优化
IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection
AI总结：
提出不一致性感知分布优化（IDO）方法，通过事实不一致性和模态不一致性建模，提升多模态假新闻检测性能。
摘要：
多模态假新闻检测旨在识别新闻的真实性。现有的多模态假新闻检测方法主要关注跨模态一致性，但往往未能明确建模欺骗性多模态内容中存在的语义不一致性。然而，虚假信息通常包含与事实不符的语义信息。为了解决这些挑战，我们提出了不一致性感知分布优化（IDO），从事实不一致性和模态不一致性的角度提高假新闻检测的性能。对于事实不一致性，我们引入通道级重加权策略以获得语义判别性嵌入，并利用高斯分布建模由事实不一致性引起的不确定性相关性。对于模态不一致性，我们利用不一致性对比学习来学习跨模态语义信息。实验表明，IDO达到了最先进的性能。
英文摘要：
Multimodal fake news detection aims to identify the authenticity of news. Existing multimodal fake news detection methods mainly focus on cross-modal consistency, but often fail to explicitly model the semantic incongruity that characterizes deceptive multimodal content. However, misinformation often contains semantic information incongruity with the facts. To address these challenges, we propose Incongruity-aware Distribution Optimization (IDO) to improve the performance of fake news detection from the perspectives of factual incongruity and modality incongruity. For factual incongruity, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings and utilize gaussian distribution to model the uncertain correlation caused by factual incongruity. For modality incongruity, we utilize incongruity contrastive learning to learn cross-modal semantic information. Experiments demonstrate that IDO achieves state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
