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消费信息日益流行，虚假信息视频对在线信息生态系统的健康构成了新的威胁。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给自动检测系统带来了新的独特挑战：1）多种模态带来的高度信息异质性，2）误导性视频操作与普遍存在的艺术性视频编辑之间界限模糊，以及3）因推荐系统在在线视频平台上的主导作用而产生的虚假信息传播新模式。为促进这一具有挑战性任务的研究，我们进行本综述以介绍虚假信息视频检测研究的进展。首先，我们从信号、语义和意图三个层次对虚假信息视频进行分析和表征。基于该表征，我们系统性地回顾了现有研究工作，涵盖从多种模态特征到线索整合技术等各个方面。我们还介绍了现有资源，包括代表性数据集和广泛使用的工具。除总结现有研究外，我们讨论了相关领域，并概述了开放问题与未来方向，以鼓励和引导更多关于虚假信息视频检测的研究。我们的公开代码仓库见 https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection。