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GB/T 45429-2025: Forensic sciences - Forged video and image of a person - General rules for examination
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| GB/T 45429-2025 | English | 279 |
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Forensic sciences - Forged video and image of a person - General rules for examination
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GB/T 45429-2025
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Basic data | Standard ID | GB/T 45429-2025 (GB/T45429-2025) | | Description (Translated English) | Forensic sciences - Forged video and image of a person - General rules for examination | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | A92 | | Classification of International Standard | 07.140 | | Word Count Estimation | 14,113 | | Date of Issue | 2025-02-28 | | Date of Implementation | 2025-06-01 | | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration | GB/T 45429-2025: Forensic sciences - Forged video and image of a person - General rules for examination
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Forensic sciences - Forged video and image of a person - General rules for examination
General principles for forensic examination of forged portraits
Forensic sciences-Forged video and image of a person-
General rules for examination
ICS 07.140
CCS A 92
National Standard of the People's Republic of China
Released on 2025-02-28
2025-06-01 Implementation
State Administration for Market Regulation
The National Standardization Administration issued
Table of Contents
Preface ...
1 Scope ...
2 Normative referenced documents ...
3 Terms and Definitions ...
4 Fake portrait categories ...
4.1 Falsification of Portraits ...
4.2 Deep Fake Portraits ...
Information Engineering Institute, Beijing University of Posts and Telecommunications, Supreme People's Procuratorate Procuratorial Technology Information Research Center, Beijing Municipal State Security Bureau Judicial Appraisal
Center, Forensic Science Research Institute, Guangzhou Criminal Science and Technology Research Institute, Shandong Provincial Public Security Department Physical Evidence Identification Research Center, Daqing Public Security Department
Security Bureau, Beijing Multi-Dimensional Video Communication Technology Co., Ltd., and Shanghai SenseTime Intelligent Technology Co., Ltd.
The main drafters of this document are. Zhang Ning, Liu Shuo, Zhai Wanfeng, Hua Feng, Liu Haotian, Hu Xiyuan, Xie Lanchi, Du Wenyu, Bai Ruwen,
Li Yali, Yu Min, Shi Lin, He Zhaofeng, Jiang Lan, Wang Kui, Zhou Songdong, Chao Feng, Lin Jiancheng, Shi Shaopei, Zeng Jinhua, Wang Guansan,
Yu Junjie, Jia Shiyou, Xiu Ningbo, and Chen Xiaojuan.
General principles for forensic examination of forged portraits
1 Scope
This document defines the terminology and classification of forged portraits in the field of forensic science, and specifies the testing equipment, testing procedures, and testing methods for forged portraits.
Content and appraisal opinion categories describe the basic methods of forged portrait inspection.
This document is applicable to forged portrait detection in the field of forensic science.
2 Normative references
The contents of the following documents constitute the essential clauses of this document through normative references in this document.
For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies.
in this document.
GB/T 45428 Forensic Science Multispectral Inspection of Forged Portraits
GB/T 45430 Forensic Science Forged Portrait Deep Fake Inspection
GB/T 45432 Forensic Science Fake Portrait Source Inspection
GB/T 45433 Forensic Science Fake Portrait Explainability Test Feature Set
GA/T 120 Terminology for forensic video image examination
3 Terms and definitions
For the purposes of this document, the terms and definitions defined in GA/T 120 and the following apply.
3.1
forged video and image of a person
Fake portraits created using a variety of technical means.
Note. This includes tampered portraits, deep fake portraits, adversarial attack portraits, and presentation of attack portraits, etc.
3.2
digital media
A video image created or captured digitally and composed of a series of pixels.
3.3
Deepfake
Technical methods that use deep learning, virtual reality, and other generative synthetic algorithms to create fake digital media.
3.4
adversarial attack
An attack that intentionally adds subtle perturbations to the input video image to evade face detection or produce false identity verification results.
means.
3.5
presentation attack
Presenting a false image of a person in the form of a paper photo, electronic photo, video, mask, head model or other physical medium for the purpose of deceiving
An attack vector designed to mislead an observer or an authentication system.
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