With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem. Though previous studies have made much progress in detecting misinformation in text and image formats, video-based misinformation brings new and unique challenges to automatic detection systems: 1) high information heterogeneity brought by various modalities, 2) blurred distinction between misleading video manipulation and nonmalicious artistic video editing, and 3) new patterns of misinformation propagation due to the dominant role of recommendation systems on online video platforms. To facilitate research on this challenging task, we conduct this survey to present advances in misinformation video detection. We first analyze and characterize the misinformation video from three levels including signals, semantics, and intents. Based on the characterization, we systematically review existing works for detection from features of various modalities to techniques for clue integration. We also introduce existing resources including representative datasets and useful tools. Besides summarizing existing studies, we discuss related areas and outline open issues and future directions to encourage and guide more research on misinformation video detection. The corresponding repository is at https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection.

翻译：随着通过在线视频流进行信息消费日益普及，虚假信息视频对在线信息生态系统的健康构成新的威胁。尽管以往研究在文本和图像格式的虚假信息检测方面取得了长足进展，但基于视频的虚假信息为自动检测系统带来新的独特挑战：1）多种模态带来的高度信息异质性，2）误导性视频操控与非恶意艺术性视频编辑之间界限模糊，3）在线视频平台上推荐系统的主导地位导致虚假信息传播的新模式。为促进这一挑战性任务的研究，本文开展综述以呈现虚假信息视频检测领域的最新进展。我们首先从信号、语义和意图三个层面分析并描述虚假信息视频的特征。基于这一特征描述，我们系统性地回顾从多模态特征到线索整合技术的现有检测工作。同时介绍现有资源，包括代表性数据集和实用工具。除总结现有研究外，我们讨论相关领域并概述开放问题与未来方向，以鼓励和指导更多关于虚假信息视频检测的研究。对应资源库位于 https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection。