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.