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计算机技术21年13期

基于深度学习的数字图像隐写和隐写分析术研究概述
谭艳萍,罗永,张俊
(贵州商学院,贵州 贵阳 550014)

摘  要:隐写术的不断发展使得隐写分析术面临的挑战越来越大。近年来,有不少学者围绕深度学习网络强大的图像特征表达学习能力进行隐写分析术研究,从而避开人工设计分类特征,减少人的参与度,用数据和算法驱动机器去实现数字图像是否含密的判定。本文将从数字图像的全局和局部统计分布特性这两个方面出发,梳理传统的和基于深度学习的隐写与隐写分析术在空域和 JPEG 域上的研究进展,并对数字图像隐写和隐写分析术未来发展方向做简要讨论。


关键词:深度学习;隐写术;隐写分析术;统计分布特性;数字图像



DOI:10.19850/j.cnki.2096-4706.2021.13.017


基金项目:贵州省教育厅青年科技人才 成长项目(黔教合 KY 字[2017]226);教育部第二批国家级新工科研究与实践项目(E-D XKJC20200524);2020 年大学生创新创业训 练计划项目(202011731005)


中图分类号:TP391.4;TP183                             文献标识码:A                               文章编号:2096-4706(2021)13-0068-05


Study Overview of Digital Image Steganography and Steganalysis Based on Deep Learning

TAN Yanping, LUO Yong, ZHANG Jun

(Guizhou University of Commerce, Guiyang 550014, China)

Abstract: With the continuous development of steganography, steganalysis is facing more and more challenges. In recent years, many scholars have carried out steganalysis research around the powerful image feature expression learning ability of deep learning network, so as to avoid manually designing classification features, reduce people’s participation, and drive the machine with data and algorithm to determine whether the digital image contains secret. Starting from the global and local statistical distribution characteristics of digital images, this paper combs the research progress of traditional and deep learning based steganography and steganalysisg in spatial domain and JPEG domain, and briefly discusses the future development direction of digital image steganography and steganalysis.

Keywords: deep learning; steganography; steganalysis; statistical distribution characteristics; digital image


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作者简介:谭艳萍(1990—),女,汉族,湖南邵阳人,讲师, 硕士研究生,研究方向:数字图像隐写与隐写分析;罗永(1991—), 男,汉族,贵州毕节人,讲师,硕士研究生,研究方向:物联网技 术应用;张俊(1987—),男,汉族,河南信阳人,副教授,博士, 研究方向:物联网技术应用。