基于深度学习的数字图像取证技术研究进展

乔通; 姚宏伟; 潘彬民; 徐明; 陈艳利

doi:10.11959/j.issn.2096-109x.2021047

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基于深度学习的数字图像取证技术研究进展

专栏Ⅰ：语音图像与音视频处理 | 更新时间：2024-06-03

- 基于深度学习的数字图像取证技术研究进展
- Research progress of digital image forensic techniques based on deep learning
- 网络与信息安全学报 2021年7卷第5期页码：13-28
- 作者机构：
  
  1. 杭州电子科技大学网络空间安全学院，浙江杭州 310018
  2. 法国特鲁瓦工程技术大学，特鲁瓦 10000
- 作者简介：
  
  [ "乔通（1986− ），男，河南新乡人，博士，杭州电子科技大学副教授，主要研究方向为数字图像取证、信息隐藏" ]
  [ "姚宏伟（1993− ），男，福建泉州人，杭州电子科技大学硕士生，主要研究方向为数字图像取证、人工智能安全" ]
  [ "潘彬民（2000− ），男，浙江温州人，主要研究方向为图像隐写分析" ]
  [ "徐明（1972− ），男，浙江杭州人，博士，杭州电子科技大学教授、博士生导师。主要研究方向为数字图像取证、网络安全" ]
  [ "陈艳利（1992− ），女，山东青岛人，法国特鲁瓦工程技术大学博士生，主要研究方向为数字图像取证" ]
- 基金信息：
  
  浙江省属高校基本科研业务费专项资金;The Fundamental Research Funds for the Provincial Universities of Zhejiang(GK219909299001-007);国家自然科学基金;The National Natural Science Foundation of China(61702150);浙江省基础公益研究项目;The Public Research Project of Zhejiang Province, China(LGG19F020015);网络空间安全重点专项基金;The Cyberspace Security Major Program in National Key Research and Development Plan of China(2016YFB0800201)
- DOI：10.11959/j.issn.2096-109x.2021047
  中图分类号： TP37
- 网络首发：2021-10，
  
  纸质出版：2021-10-15
- 稿件说明：
移动端阅览
乔通, 姚宏伟, 潘彬民, 等. 基于深度学习的数字图像取证技术研究进展[J]. 网络与信息安全学报, 2021,7(5):13-28.

Tong QIAO, Hongwei YAO, Binmin PAN, et al. Research progress of digital image forensic techniques based on deep learning[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 13-28.
乔通, 姚宏伟, 潘彬民, 等. 基于深度学习的数字图像取证技术研究进展[J]. 网络与信息安全学报, 2021,7(5):13-28. DOI： 10.11959/j.issn.2096-109x.2021047.

Tong QIAO, Hongwei YAO, Binmin PAN, et al. Research progress of digital image forensic techniques based on deep learning[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 13-28. DOI： 10.11959/j.issn.2096-109x.2021047.

摘要

随着数字图像篡改技术不断的革新换代，传统的取证方法已经无法对抗最新的多媒体篡改手段和技术，尤其是深度造假及深度学习技术带来的全新挑战。总结提炼了包括图像预处理模块、特征提取模块及分类结果后处理模块的通用数字图像取证框架，并在提出的框架基础之上分析现有相关研究的优缺点，同时归纳了数字图像取证面临的挑战并指明未来的发展方向。

Abstract

In the new era of rapid development of internet

where massive forgery images with updated tampering techniques flood into

traditional algorithms are no longer able to deal with the latest multimedia tampering techniques

especially those caused by Deepfake and deep learning techniques.Thus

a universal framework for image forensics including image pre-processing module

feature extraction module and post-processing module designed for specific classification were proposed creatively.Accordingly

the state-of-the-art algorithms were reviewed，and meanwhile the main strength and limitations of current algorithms were generalized.More importantly

the future studies were also listed for advancing the development of digital image forensics.

关键词

Keywords

references

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