网络与信息安全学报 ›› 2021, Vol. 7 ›› Issue (5): 13-28.doi: 10.11959/j.issn.2096-109x.2021047
乔通1, 姚宏伟1, 潘彬民1, 徐明1, 陈艳利2
修回日期:
2020-09-25
出版日期:
2021-10-15
发布日期:
2021-10-01
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、信息隐藏基金资助:
Tong QIAO1, Hongwei YAO1, Binmin PAN1, Ming XU1, Yanli CHEN2
Revised:
2020-09-25
Online:
2021-10-15
Published:
2021-10-01
Supported by:
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。
中图分类号:
乔通, 姚宏伟, 潘彬民, 徐明, 陈艳利. 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, Ming XU, Yanli CHEN. Research progress of digital image forensic techniques based on deep learning[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 13-28.
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