重新思考生成式人工智能时代的多模态假新闻检测

中文摘要 AI 辅助

生成式内容正日益进入新闻的生产和传播过程，将假新闻从人工捏造或简单篡改的材料转变为原生内容与生成内容共同参与的复杂形式。现有的多模态假新闻检测研究主要关注真实性评估，很少刻画生成性差异如何影响证据的可靠性。相比之下，AIGC检测主要确定内容是否由生成模型生成或修改，但其本身并不能确定底层新闻事件是否真实。为了在数据和评估上弥合这些任务之间的分离，我们构建了Weibo26，一个面向生成内容场景的多模态假新闻检测数据集。在此基础上，我们提出了生成性感知的层次推理（GAHR）框架，该框架将全局判断与局部修正相结合，使生成性信息参与新闻真实性推理。在多个现有假新闻检测基准和Weibo26上的实验表明，GAHR在有效识别生成内容的同时，实现了具有竞争力的真实性检测性能。

英文摘要

Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipulated material into complex forms in which native and generated content jointly participate. Existing multimodal fake news detection research primarily focuses on veracity assessment and rarely characterizes how generativity differences affect the reliability of evidence. In contrast, AIGC detection primarily determines whether content is generated or modified by generative models, but it does not by itself establish whether the underlying news event is true. To bridge the separation between these tasks in data and evaluation, we construct Weibo26, a multimodal fake news detection dataset for generative-content scenarios. On this basis, we propose the Generativity-Aware Hierarchical Reasoning (GAHR) framework, which combines global judgment with local correction so that generativity information participates in news-veracity reasoning. Experiments on multiple existing fake news detection benchmarks and Weibo26 show that GAHR achieves competitive veracity-detection performance while effectively identifying generative content.

发表机构

• Nanjing University of Science and Technology（南京理工大学）
• University of Chinese Academy of Sciences（中国科学院大学）

机构由 AI 辅助整理，请以论文原文为准。