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 ubiquitous 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 research. 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 widely used 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. Our corresponding public repository is available 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了新威胁。尽管先前研究在检测文本和图像格式的虚假信息方面取得了诸多进展，但基于视频的虚假信息为自动检测系统带来了新的独特挑战：1）多模态带来的高度信息异质性；2）误导性视频操纵与普遍存在的艺术性视频编辑之间的界限模糊；3）在线视频平台推荐系统的主导作用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，我们撰写此综述以呈现虚假信息视频检测研究的最新进展。首先从信号、语义和意图三个层面分析并刻画虚假信息视频的特征。基于该特征刻画，我们系统回顾了现有检测研究，涵盖从多模态特征到线索整合技术的各个方面。同时介绍现有资源，包括代表性数据集和广泛使用的工具。除总结现有研究外，我们探讨了相关领域，并概述了开放问题与未来方向，以期鼓励并引导更多关于虚假信息视频检测的研究。我们的相关公共资源库可通过https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection访问。