从传统到深度学习的图像隐写技术研究  

Image Steganography Methods from Traditional to Deep Learning

作　　者：周琳娜[1] 曹洋 Zhou Linna;Cao Yang(University of International Relations,Beijing 100091)

机构地区：[1]国际关系学院,北京100091

出　　处：《信息安全研究》2019年第3期230-235,共6页Journal of Information Security Research

基　　金：国家自然科学基金重点项目(U1536207);国家重点研发计划项目(2016QY08D1600);国家重点研发计划课题(2016YFB0801405)

摘　　要：通过归纳总结典型的传统嵌入式图像隐写算法和基于深度学习的新式非嵌入式图像隐写算法机制,指出在该领域中,传统式难以抵抗当前基于先进机器学习的隐写分析技术,而新式存在嵌入容量不高、嵌入过程比较复杂等问题.进而提出基于无嵌入式思路设计的生成对抗网络或者改进后的深度卷积生成对抗网路,将传统和新式算法结合统一,互相弥补不足,进一步发展图像隐写技术.This paper summarizes the schemes of typical traditional embedded image steganography and new non-embedded image steganography algorithm based on deep learning,and points out that the traditional method is difficult to resist the current state-of-the-art steganalysis based on machine learning in this field,and the embedding capacity of new method is not enough,the embedding process is more complicated.Then the design of steganography without embedding(SWE)based on the generative adversarial networks or deep convolutional generative adversarial networks is proposed.Combining traditional and new algorithms and compensating for each other,the image steganography gets further development.

关 键 词：图像隐写 嵌入式隐写 无嵌入式隐写 深度学习 对抗生成网络 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]