Title: A New Dataset and Benchmark for Grounding Multimodal Misinformation Show Chinese title

Title: 一种新的数据集和基准用于定位多模态虚假信息

Abstract: The proliferation of online misinformation videos poses serious societal risks. Current datasets and detection methods primarily target binary classification or single-modality localization based on post-processed data, lacking the interpretability needed to counter persuasive misinformation. In this paper, we introduce the task of Grounding Multimodal Misinformation (GroundMM), which verifies multimodal content and localizes misleading segments across modalities. We present the first real-world dataset for this task, GroundLie360, featuring a taxonomy of misinformation types, fine-grained annotations across text, speech, and visuals, and validation with Snopes evidence and annotator reasoning. We also propose a VLM-based, QA-driven baseline, FakeMark, using single- and cross-modal cues for effective detection and grounding. Our experiments highlight the challenges of this task and lay a foundation for explainable multimodal misinformation detection. Show Chinese abstract

Abstract: 在线虚假信息视频的泛滥对社会构成了严重的风险。 当前的数据集和检测方法主要针对基于后处理数据的二分类或单模态定位，缺乏对抗有说服力的虚假信息所需的可解释性。 在本文中，我们引入了接地多模态虚假信息（Grounding Multimodal Misinformation, GroundMM）的任务，该任务验证多模态内容并在不同模态中定位误导性片段。 我们提出了第一个针对此任务的真实世界数据集，GroundLie360，该数据集具有虚假信息类型的分类体系，文本、语音和视觉方面的细粒度标注，并通过Snopes证据和标注者推理进行验证。 我们还提出了一种基于视觉语言模型（VLM）的、问答驱动的基线方法FakeMark，利用单模态和跨模态线索进行有效的检测和定位。 我们的实验突显了该任务的挑战，并为可解释的多模态虚假信息检测奠定了基础。