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基于帧间量化参数强度值检测深度伪造人像视频

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深度学习算法的发展为伪造人像视频的创作增添了"助力",应运而生的深度伪造检测成为学界和业界关注的焦点.基于深度学习的伪造视频检测技术需要耗费大量时间与算力训练分类器,深度学习网络的黑盒性和不可解释性也困扰着深度伪造人像视频取证研究.围绕深度伪造人像视频的真实性取证问题,本文提出一种基于视频帧间量化参数强度值的检测方法,并通过二元Logistic回归方程给出参考阈值.实验结果表明:本文的方法在检测利用DeepFaceLab换脸平台合成的深度伪造人像视频中表现出良好的准确率和鲁棒性.所提方法有助于对可疑人像视频真实性进行判断,为涉案人像视频提供取证依据.
Detection of Deepfake Portrait Videos Based on Interframe Quantization Parameter Intensity Value
With the breakthrough of deep-learning technology in the field of artificial intelligence,deepfake portrait videos appear more and more frequently,such as facial tampering,pornographic video face swapping,changing politicians'faces and making false statements,etc.This kind of deepfakes may pose a threat to societies;therefore,distinguishing deepfake videos from genuine ones has become an urgen issue.Lots of deepfake detection methods are carried out by constructing many data sets with different compression factors.At present,the deepfake detection technology based on deep-learning algorithm is popular,which requires lots of significant time consumption and massive computing power for training classification model.At the same time,the attributes of black box and unexplainability of deep learning networks also plague the researchers in forensic science.In order to solve the problem of authenticity forensics of the deepfake portrait videos,this paper takes portrait videos encoded by H.264/AVC as the research object,and proposes a method based on inter-frame quantization parameter intensity value to detect deepfake portrait videos and real portrait videos.The selection of inter-frame quantization parameter intensity value and the determination of the inter-frame quantization parameter intensity by binary Logistic regression equation are expounded in detail.The experimental results show favorable accuracy and robustness for the deepfake portrait videos synthesized by DeepFaceLab platform.The paper proposed an interpretable detection method for deepfake portrait videos,which is conducive to determine the direction of investigation and confirm the criminal facts.But there are some limitations.Firstly,the experimental samples are not rich enough.Secondly,the introduced method is greatly affected by video compression,which caused limited application scenarios.Thirdly,the analysis efficiency needs to be optimized.

video investigationdeepfake portraitrelationship between video framesquantization parameterbinary Logistic regression equation

李梓楷、王宇飞、廖广军、郭晶晶、刘光尧

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广东警官学院刑事技术系,广州 510440

公安部鉴定中心,北京 100038

视频侦查 深度伪造人像 视频帧间关系 量化参数 二元Logistic回归方程

广东省重点建设学科科研能力提升项目痕迹科学与技术公安部重点实验室开放课题教育部科技部司法鉴定技术应用与社会治理学科创新引智基地项目广东省科技创新战略专项资金(大学生科技创新培育)项目

2021ZDJS0472021FMKFKT07B200772022GDSKJCX034

2024

10.16467/j.1008-3650.2023.0027

刑事技术
公安部物证鉴定中心

刑事技术

CSTPCD
影响因子：0.315
ISSN：1008-3650
年,卷(期)：2024.49(1)
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