数字内容生成、检测与取证技术综述

doi:10.11959/j.issn.2096-0271.2023066

大数据 ›› 2023, Vol. 9 ›› Issue (5): 150-173.doi: 10.11959/j.issn.2096-0271.2023066

数字内容生成、检测与取证技术综述

曹娟¹^,², 朱勇椿¹^,², 亓鹏¹^,², 黄子尧¹^,², 杨天韵¹^,², 王政嘉¹^,², 卜语嫣¹^,²

¹ 中国科学院计算技术研究所数字内容合成与伪造检测实验室，北京 100190
² 中国科学院大学，北京 100049

出版日期:2023-09-15 发布日期:2023-09-01
作者简介:曹娟（1980- ），女，博士，中国科学院计算技术研究所研究员、前瞻研究实验室主任、数字内容合成与伪造检测实验室主任，中国科学院大学岗位教授，中国科学院计算技术研究所“十四五”规划重点研究方向“数字内容合成与伪造检测”方向牵头人。主要从事多媒体数字内容分析与伪造检测相关的研究工作。作为第一完成人，成果入选2022年世界互联网大会领先科技成果；获得2020年北京市科学技术进步奖一等奖、2020年北京市三八红旗奖章及2021年中国人工智能大赛“创新人物”和“创新之星”称号。作为项目负责人，围绕多媒体内容安全方向承担十余项国家级重要课题
朱勇椿（1996- ），男，博士，2023年毕业于中国科学院计算技术研究所，主要研究方向为迁移学习、推荐系统、虚假新闻检测
亓鹏（1996- ），女，博士，2023年毕业于中国科学院计算技术研究所，主要研究方向为虚假信息检测、多媒体内容分析
黄子尧（1995- ），男，中国科学院计算技术研究所博士生，主要研究方向为数字人合成技术
杨天韵（1997- ），女，中国科学院计算技术研究所博士生，主要研究方向为深度生成模型溯源、人工智能安全
王政嘉（1998- ），女，中国科学院计算技术研究所博士生，主要研究方向为可解释虚假信息检测
卜语嫣（2000- ），女，中国科学院计算技术研究所硕士生，主要研究方向为多模态虚假信息检测
基金资助:
国家自然科学基金资助项目(62203425);中国科学院项目(E141020);中国博士后科学基金特别资助(2022TQ0344);博士后国际交流计划引进项目(YJ20220198)

A survey on digital content generation, detection, and forensics techniques

Juan CAO¹^,², Yongchun ZHU¹^,², Peng QI¹^,², Ziyao HUANG¹^,², Tianyun YANG¹^,², Zhengjia WANG¹^,², Yuyan BU¹^,²

¹ Media Synthesis and Forensics Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
² University of Chinese Academy of Sciences, Beijing 100049, China

Online:2023-09-15 Published:2023-09-01
Supported by:
The National Natural Science Foundation of China(62203425);The Project of Chinese Academy of Sciences(E141020);The China Postdoctoral Science Foundation(2022TQ0344);The International Postdoctoral Exchange Fellowship Program by Office of China Postdoc Council(YJ20220198)

摘要/Abstract

摘要：

近年来，数字生成内容技术得到了极大的发展，数字内容的检测和取证技术面临新的挑战。首先从自然语言大模型、视觉生成技术、多模态生成技术3个方面介绍数字内容生成技术，从生成文本检测、生成图片检测、生成音视频检测3个方面介绍数字内容检测技术，从利用事实信息和伪造痕迹两方面介绍数字内容取证技术；接着介绍这些技术的应用场景；最后对该研究领域的未来工作进行展望，指出几个需要重点关注的方向。

关键词: 数字内容, 生成技术, 检测应用, 取证技术

Abstract:

In recent years, the technology of digital content generation has been greatly developed, and the detection and forensic technology of digital content are facing new challenges.This paper firstly introduced digital content generation technology from three aspects: large natural language model, visual generation technology, and multimodal generation technology.Secondly, it introduced digital content detection technology from three aspects: generated text detection, generated image detection, and generated audio and video detection.Thirdly, it introduced digital content forensics technology from two aspects: utilizing fact ual information and forging traces.Then, this paper introduced the application scenarios of these techniques.Finally, it prospected the future work in this research field, and pointed out several directions that need to be focused on.

Key words: digital content, generation technology, detection, application, forensics technology

中图分类号:

TP316

曹娟, 朱勇椿, 亓鹏, 黄子尧, 杨天韵, 王政嘉, 卜语嫣. 数字内容生成、检测与取证技术综述[J]. 大数据, 2023, 9(5): 150-173.

Juan CAO, Yongchun ZHU, Peng QI, Ziyao HUANG, Tianyun YANG, Zhengjia WANG, Yuyan BU. A survey on digital content generation, detection, and forensics techniques[J]. Big Data Research, 2023, 9(5): 150-173.

图/表 4

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数字内容生成、检测与取证技术综述

A survey on digital content generation, detection, and forensics techniques

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数字内容生成、检测与取证技术综述

A survey on digital content generation, detection, and forensics techniques

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