摘要： 近年来，深度学习在计算机视觉以及语音信号处理等领域取得了重大成功。然而，深度学习的飞速发展也带来了负面影响，各类伪造视频，语音在网络上泛滥成灾，一些不法分子利用深度学习技术替换原始视频的人脸、编辑面部属性、合成说话人语音、克隆语音，通过制作色情视频、虚假新闻、政治谣言等造成社会动荡、混乱，威胁个人利益、国家安全。为了消除这些负面影响，众多学者从不同的角度提出解决方案。早期伪造主要集中在单模态伪造，因此，目前大多数解决方案侧重单模态的伪造识别问题，未能充分考虑音频和视频之间的内在联系，现有的单模态鉴伪方式在处理音频与视频均被伪造的情况时常常表现出次优的识别性能。近期，随着研究的深入，部分学者开始探索使用多模态模型鉴伪，取得了显著的成果。本综述回顾了视频、语音伪造及鉴伪技术，收集整理了视频、语音、音视频伪造数据集，并总结归纳了多模态鉴伪方法。最后对如今检测技术存在的问题和研究方向进行了分析并给出建议。

Abstract: Deep learning has achieved significant success in fields of computer vision and speech signal processing. However, the rapid development of deep learning has also brought negative impacts. All kinds of fake videos and voices are flooding on the Internet. Some criminals use deep learning technology to replace the face of the original video, edit facial attributes, synthesize the speaker's voice, and clone speaker's voices. Criminals can cause social unrest and chaos by producing pornographic videos, fake news, political rumors, etc., threatening personal interests and national security. Many scholars have proposed solutions from different perspectives to eliminate these negative effects. Early forgery mainly focused on single-modal forgery. Therefore, most current solutions focus on single-modal forgery recognition problems and fail to consider the intrinsic relationship between audio and video fully. Existing single-modal detection methods often exhibit suboptimal recognition performance when both audio and video are forged. Recently, with the deepening of research, some scholars have begun to explore the use of multi-modal models for forgery detection and have achieved remarkable results. This survey reviews video, voice forgery, and forgery detection technologies, collects and sorts video, voice, audio and video forgery data sets, and summarizes multi-modal forgery detection methods. Finally, the existing problems and research directions of current detection technology are analyzed and suggestions are given.