音视频深度伪造与鉴伪综述

doi:10.19678/j.issn.1000-3428.0070700

摘要/Abstract

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。早期伪造主要集中于单模态伪造,因此目前大多数解决方案侧重于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,然而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。本综述首先回顾了视频、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,然后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。

关键词: 深度学习, 伪造, 鉴伪, 多模态, 音视频

Abstract: Deep learning has achieved significant success in computer vision and speech signal processing. However, its rapid development of deep learning has had negative effects. Many types of fake videos and voices are flooding the Internet. Some criminals use deep learning technology to replace the face in original video, edit facial attributes, and synthesize or clone the speaker's voice. Criminals can cause social unrest and chaos by producing pornographic videos, fake news, and political rumors, which threaten personal interests and national security. Many scholars have proposed solutions to eliminate these negative effects. Early forgeries focused primarily on single-modal forgeries. Therefore, most current solutions focus on single-modal counterfeit detection and fail to fully consider the intrinsic relationship between audio and video. Existing single-modal detection methods often exhibit suboptimal performance when both audio and video are forged. Recently, with the increase in research, some scholars have begun to explore the use of multimodal models for counterfeit detection and have achieved remarkable results. This survey reviews video and voice forgery and detection technologies, collects and sorts voice, audio, and video forgery datasets, and summarizes multimodal counterfeit detection methods. Finally, the existing problems and future research directions of the current detection technology are analyzed.

Key words: deep learning, forgery, counterfeit detection, multimodal, audio and video

中图分类号:

TP391

魏方达, 刘淼, 孙毅, 王晶, 赵胜辉. 音视频深度伪造与鉴伪综述[J]. 计算机工程, 2026, 52(7): 40-55.

WEI Fangda, LIU Miao, SUN Yi, WANG Jing, ZHAO Shenghui. Review of Audio and Video Deepfakes and Counterfeit Detection[J]. Computer Engineering, 2026, 52(7): 40-55.

https://www.ecice06.com/CN/Y2026/V52/I7/40

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