With the rapid advancement of deepfake technologies，including generative adversarial networks （GANs），autoencoders （AEs），and diffusion models （DMs），deepfake facial images have been increasingly exploited for malicious purposes，such as the creation of nonconsensual pornographic content，telecommunication fraud，and the propagation of political disinformation. In recent years，the continuous iteration of these core deepfake technologies has considerably lowered the technical threshold for generating high-fidelity fake facial images： GANs，with their adversarial training mechanism between generators and discriminators，enable the rapid synthesis of realistic facial features； AEs，relying on unsupervised learning and feature reconstruction capabilities，excel in simulating subtle facial expressions and skin textures； diffusion models，through step-by-step noise reduction and feature enhancement，have further broken through the limitations of traditional deepfake technologies in terms of image clarity and detail authenticity，making deepfake facial images increasingly difficult to distinguish with the naked eye and even challenging preliminary manual identification in forensic investigations. As an emerging form of digital evidence，deepfakes not only pervade contemporary criminal activities but also risk being weaponized for false accusations or the tampering of surveillance records，thereby severely undermining the reliability of facial image analysis in forensic science and threatening the integrity of the criminal justice system. To address the aforementioned challenges，this study systematically traces the developmental trajectory of key datasets and the technological evolution of deepfake facial images while conducting an in-depth dissection of the core architecture underlying their generation mechanisms，encompassing primary technical routes，theoretical underpinnings，and functional logics. In terms of dataset development，the evolution process from early small-scale，single-scene datasets （such as CelebA and LFW） to large-scale，multiscenario，high-diversity datasets （such as DFDC，FF++，and Celeb-DF） is sorted out，with the advantages and limitations of each type of dataset analyzed in terms of sample size，fake generation technology coverage，and scene diversity Moreover，the lack of standardized，forensic-oriented datasets has become one of the key bottlenecks restricting the practical application of deepfake detection technologies. In terms of technological evolution，the development of deepfake facial image technology is divided into three stages： the initial exploration stage dominated by GANs，the improvement stage supplemented by AEs，and the mature stage driven by diffusion models. The theoretical breakthroughs and technical improvements of each stage are elaborated，and the internal logic of the continuous improvement of fake image fidelity and the continuous reduction of generation costs is revealed. In the in-depth analysis of generation mechanisms，the core principles of the three mainstream technologies are focused on，the differences in their technical routes and functional characteristics are clarified，and the common key links and potential vulnerabilities in the generation process are summarized，laying a theoretical foundation for the subsequent design of targeted detection and identification methods. On the basis of 737 relevant articles retrieved and screened from the Web of Science database，a bibliometric analysis is performed via VOSviewer to identify research trends in the field and illuminate the substantial disconnect between existing detection technology research and the practical requirements for evidence authentication in judicial practice. The retrieval and screening process strictly followed the PRISMA guidelines： “deepfake”，“facial image”，“forensic science”，“identification”，and “detection” are adopted as key search terms； the retrieval time range is defined from the emergence of deepfake technology to the present； and irrelevant literature，review papers with low innovation，and duplicate publications are excluded through preliminary screening and full-text reading. The bibliometric analysis focuses on three core dimensions： research hotspots，institutional cooperation，and research frontiers. Results show that current research in the field is mainly concentrated on the optimization of detection algorithms （such as deep learning-based feature extraction and classification models），while research on the translation of detection results into forensic authentication，the formulation of forensic identification standards，and the adaptation of detection technologies to judicial practice scenarios is relatively scarce. This disconnect is mainly reflected in two aspects： on the one hand，most existing detection technologies focus on technical accuracy，ignoring the admissibility requirements of judicial evidence （such as objectivity，authenticity，and relevance）； on the other hand，forensic examiners lack effective methods to integrate technical detection results into judicial authentication，leading to the situation where advanced detection technologies fail to be effectively applied in judicial practice. In response to the identified research-practice disconnect，this study proposes a classification of deepfake facial image detection methods into two complementary frameworks： technology principle oriented and application scenario adaptive，designed to address evidence authentication needs across distinct stages. The technology-principle-oriented framework takes the technical principles of deepfake generation as the core，classifies detection methods in accordance with the technical characteristics of different generation technologies （such as GAN-based detection，AE-based detection，and diffusion model-based detection），and focuses on improving the accuracy and generalization ability of detection，thus being mainly applicable to the preliminary technical screening stage of evidence in forensic investigations. The application-scenario-adaptive framework takes the actual needs of different judicial stages （such as investigation，prosecution，and trial） as guidance，classifies detection methods in light of the scenario characteristics and evidence requirements of each stage，and focuses on enhancing the adaptability and operability of detection methods，thereby being mainly applicable to the formal evidence authentication stage in judicial practice. The two frameworks are complementary and mutually supportive： the technology-principle-oriented framework provides technical support for the application-scenario-adaptive framework，and the application-scenario-adaptive framework guides the direction of technical optimization，thus forming a complete detection system covering the entire process of forensic evidence authentication. In response to the evidentiary credibility challenges posed by deepfake facial images as a novel criminal vector，this study argues that forensic examiners should facilitate the translation of detection outcomes into forensic authentication，integrating detection results with the Bayesian inference framework to enhance their admissibility and probative force in judicial proceedings. Specifically，a Bayesian inference-based forensic authentication model is proposed. Technical detection results are taken as the prior probability，forensic examiners’ professional experience and case context are integrated to determine the likelihood ratio，and the posterior probability of the authenticity of facial images is ultimately calculated via the Bayesian formula to convert objective technical indicators into credible judicial authentication conclusions. Furthermore，forensic examiners should continuously improve their understanding of deepfake technologies，master the working principles and limitations of detection technologies，and avoid over-reliance on technical detection results to ensure the scientificity and rigor of forensic authentication. In conclusion，by bridging the advancements in computer vision technology with the practical demands of forensic evidence practice，this research articulates the logical pathway from technical detection to judicial authentication，providing a theoretically insightful and practically relevant reference for forensic science researchers and judicial practitioners alike. For forensic science researchers，this study clarifies the research direction of integrating technical research with judicial practice，identifies the key issues to be addressed in the future （such as the construction of forensic-oriented datasets，the optimization of detection algorithms adapted to judicial scenarios，and the formulation of forensic identification standards），and offers a theoretical framework for subsequent research. For judicial practitioners，this study provides a practical operational path for the forensic authentication of deepfake facial images，facilitates the better grasp of the application methods and precautions of deepfake detection technologies，and elevates the efficiency and accuracy of digital evidence authentication. Additionally，this study offers a reference for the formulation of relevant laws and regulations，which helps improve the legal system of digital evidence authentication and promote the sound development of the criminal justice system in the era of deepfake technology.
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figureFileBig=ij45YnJB3SArIZK/QoBH1w==, tableContent=null), ArticleFig(id=1322963008124747835, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322962994438734827, language=EN, label=Table 1, caption=Representative datasets of deepfake faces from 2018 to 2024, figureFileSmall=null, figureFileBig=null, tableContent=
Advances in forensic science of deepfake facial images evidence
Tianli Guo, Jisong Li, Yunqi Tang*
Affiliations
School of Criminal Investigation，People’s Public Security University of China，Beijing 100038，China
出版时间: 2026-10-16
doi: 10.11834/jig.250610
文章导航
摘要
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随着生成对抗网络（generative adversarial network，GAN）、自编码器（autoencoder，AE）和扩散模型（diffusion model，DM）等深度伪造技术的快速发展，深度伪造人脸图像广泛用于制作非自愿色情内容、实施电信诈骗及传播政治虚假信息等恶意行为。作为新型数字证据，深度伪造不仅频繁出现在当前犯罪活动中，还可能被用于虚假指控或操控监控记录，严重削弱法庭证据科学中人脸图像分析的可靠性，进而威胁刑事司法的公正性。本文系统梳理了关键数据集的发展脉络与深度伪造人脸图像的技术演进路径，深入剖析其生成机制的核心架构，涵盖主要技术路线、理论基础与功能逻辑；基于Web of Science数据库筛选出的737篇文献，采用VOSviewer进行文献计量分析，识别该领域的研究趋势，并揭示现有检测技术研究与司法实践中证据鉴伪实际需求之间的显著脱节。本研究提出将深度伪造人脸图像检测方法划分为技术原理导向与应用阶段适配两类互补框架，以应对不同阶段的证据鉴定需求。针对深度伪造人脸图像作为新型犯罪载体所带来的证据可信性挑战，本文主张法庭证据鉴定人应推动检测向鉴定的转化，实现检测结果与贝叶斯推理框架的融合，提升其在司法证明中的可采性与说服力。本研究衔接计算机视觉技术发展与法庭证据实践需求，明确了从技术检测到司法鉴定的逻辑路径，为法庭科学研究人员与司法实务工作者提供了具有技术价值的理论参考。
关键词
司法
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证据
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人脸图像
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深度伪造
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检测
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鉴定
Abstract
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With the rapid advancement of deepfake technologies，including generative adversarial networks （GANs），autoencoders （AEs），and diffusion models （DMs），deepfake facial images have been increasingly exploited for malicious purposes，such as the creation of nonconsensual pornographic content，telecommunication fraud，and the propagation of political disinformation. In recent years，the continuous iteration of these core deepfake technologies has considerably lowered the technical threshold for generating high-fidelity fake facial images： GANs，with their adversarial training mechanism between generators and discriminators，enable the rapid synthesis of realistic facial features； AEs，relying on unsupervised learning and feature reconstruction capabilities，excel in simulating subtle facial expressions and skin textures； diffusion models，through step-by-step noise reduction and feature enhancement，have further broken through the limitations of traditional deepfake technologies in terms of image clarity and detail authenticity，making deepfake facial images increasingly difficult to distinguish with the naked eye and even challenging preliminary manual identification in forensic investigations. As an emerging form of digital evidence，deepfakes not only pervade contemporary criminal activities but also risk being weaponized for false accusations or the tampering of surveillance records，thereby severely undermining the reliability of facial image analysis in forensic science and threatening the integrity of the criminal justice system. To address the aforementioned challenges，this study systematically traces the developmental trajectory of key datasets and the technological evolution of deepfake facial images while conducting an in-depth dissection of the core architecture underlying their generation mechanisms，encompassing primary technical routes，theoretical underpinnings，and functional logics. In terms of dataset development，the evolution process from early small-scale，single-scene datasets （such as CelebA and LFW） to large-scale，multiscenario，high-diversity datasets （such as DFDC，FF++，and Celeb-DF） is sorted out，with the advantages and limitations of each type of dataset analyzed in terms of sample size，fake generation technology coverage，and scene diversity Moreover，the lack of standardized，forensic-oriented datasets has become one of the key bottlenecks restricting the practical application of deepfake detection technologies. In terms of technological evolution，the development of deepfake facial image technology is divided into three stages： the initial exploration stage dominated by GANs，the improvement stage supplemented by AEs，and the mature stage driven by diffusion models. The theoretical breakthroughs and technical improvements of each stage are elaborated，and the internal logic of the continuous improvement of fake image fidelity and the continuous reduction of generation costs is revealed. In the in-depth analysis of generation mechanisms，the core principles of the three mainstream technologies are focused on，the differences in their technical routes and functional characteristics are clarified，and the common key links and potential vulnerabilities in the generation process are summarized，laying a theoretical foundation for the subsequent design of targeted detection and identification methods. On the basis of 737 relevant articles retrieved and screened from the Web of Science database，a bibliometric analysis is performed via VOSviewer to identify research trends in the field and illuminate the substantial disconnect between existing detection technology research and the practical requirements for evidence authentication in judicial practice. The retrieval and screening process strictly followed the PRISMA guidelines： “deepfake”，“facial image”，“forensic science”，“identification”，and “detection” are adopted as key search terms； the retrieval time range is defined from the emergence of deepfake technology to the present； and irrelevant literature，review papers with low innovation，and duplicate publications are excluded through preliminary screening and full-text reading. The bibliometric analysis focuses on three core dimensions： research hotspots，institutional cooperation，and research frontiers. Results show that current research in the field is mainly concentrated on the optimization of detection algorithms （such as deep learning-based feature extraction and classification models），while research on the translation of detection results into forensic authentication，the formulation of forensic identification standards，and the adaptation of detection technologies to judicial practice scenarios is relatively scarce. This disconnect is mainly reflected in two aspects： on the one hand，most existing detection technologies focus on technical accuracy，ignoring the admissibility requirements of judicial evidence （such as objectivity，authenticity，and relevance）； on the other hand，forensic examiners lack effective methods to integrate technical detection results into judicial authentication，leading to the situation where advanced detection technologies fail to be effectively applied in judicial practice. In response to the identified research-practice disconnect，this study proposes a classification of deepfake facial image detection methods into two complementary frameworks： technology principle oriented and application scenario adaptive，designed to address evidence authentication needs across distinct stages. The technology-principle-oriented framework takes the technical principles of deepfake generation as the core，classifies detection methods in accordance with the technical characteristics of different generation technologies （such as GAN-based detection，AE-based detection，and diffusion model-based detection），and focuses on improving the accuracy and generalization ability of detection，thus being mainly applicable to the preliminary technical screening stage of evidence in forensic investigations. The application-scenario-adaptive framework takes the actual needs of different judicial stages （such as investigation，prosecution，and trial） as guidance，classifies detection methods in light of the scenario characteristics and evidence requirements of each stage，and focuses on enhancing the adaptability and operability of detection methods，thereby being mainly applicable to the formal evidence authentication stage in judicial practice. The two frameworks are complementary and mutually supportive： the technology-principle-oriented framework provides technical support for the application-scenario-adaptive framework，and the application-scenario-adaptive framework guides the direction of technical optimization，thus forming a complete detection system covering the entire process of forensic evidence authentication. In response to the evidentiary credibility challenges posed by deepfake facial images as a novel criminal vector，this study argues that forensic examiners should facilitate the translation of detection outcomes into forensic authentication，integrating detection results with the Bayesian inference framework to enhance their admissibility and probative force in judicial proceedings. Specifically，a Bayesian inference-based forensic authentication model is proposed. Technical detection results are taken as the prior probability，forensic examiners’ professional experience and case context are integrated to determine the likelihood ratio，and the posterior probability of the authenticity of facial images is ultimately calculated via the Bayesian formula to convert objective technical indicators into credible judicial authentication conclusions. Furthermore，forensic examiners should continuously improve their understanding of deepfake technologies，master the working principles and limitations of detection technologies，and avoid over-reliance on technical detection results to ensure the scientificity and rigor of forensic authentication. In conclusion，by bridging the advancements in computer vision technology with the practical demands of forensic evidence practice，this research articulates the logical pathway from technical detection to judicial authentication，providing a theoretically insightful and practically relevant reference for forensic science researchers and judicial practitioners alike. For forensic science researchers，this study clarifies the research direction of integrating technical research with judicial practice，identifies the key issues to be addressed in the future （such as the construction of forensic-oriented datasets，the optimization of detection algorithms adapted to judicial scenarios，and the formulation of forensic identification standards），and offers a theoretical framework for subsequent research. For judicial practitioners，this study provides a practical operational path for the forensic authentication of deepfake facial images，facilitates the better grasp of the application methods and precautions of deepfake detection technologies，and elevates the efficiency and accuracy of digital evidence authentication. Additionally，this study offers a reference for the formulation of relevant laws and regulations，which helps improve the legal system of digital evidence authentication and promote the sound development of the criminal justice system in the era of deepfake technology.
尽管不同类型信息的具体表示与特征提取方法较为复杂，但图2中的各子图仍能直观反映不同模态的基本分布与结构。其中，语义掩码通过像素级标注实现结构化面部分割，明确眼、鼻和口等关键面部部位的边缘轮廓与边界，完成空间定位与语义区分；颜色与深度信息互补，颜色通道反映表面外观（如色彩分布与纹理），深度数据则通过空间距离刻画 3D 面部几何特征（如鼻梁凸起、下颌凹陷），二者共同提供多模态的面部外观描述；在频域分析中，高频成分体现皮肤纹理、睫毛等细节，中频成分连接局部结构与整体形态，低频成分主导全局轮廓与宏观布局（如脸型、五官排列），反映粗粒度结构特征；空域特征则随维度变化而呈现不同表征能力。在二维空间中，像素梯度与密度可以刻画局部复杂性，例如眼部区域因纹理更为丰富而表现出更剧烈的梯度变化；在三维空间中，不同坐标轴用于捕捉特定几何属性，z轴聚焦于深度方向的形变，x/y轴描述平面内的轮廓与纹理，从而形成多视角的空间表达。
本研究以“（“deepfake” OR “face forgery” OR “face manipulation” OR “face swap” OR “facial deepfake”） AND （“face” OR “facial”）”为主题，在Web of Science核心合集（core collection）检索近5年文献，共获取845篇文档；经筛选排除与深度伪造人脸不相关的内容及综述类文章后，剩余737篇有效文献。保留文献的题目与摘要信息并导入VOSviewer，以Binary Counting方式计算关键词频数，选取频数大于20的关键词共196个，展示前60%（共118个）；将标签最大长度设置为10、线长设置为1 000，最终生成网络可视化图谱。
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doi: 10.11834/jig.250610
接收时间：2025-12-15
首发时间：2026-10-30
出版时间：2026-10-16
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收稿日期：2025-12-15
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Major Project of the National Social Science Fund of China （25&ZD238）；Graduate Research Project of People’s Public Security University of China （2024yjsky032）
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