从传统到深度学习的图像隐写技术研究

信息安全研究 ›› 2019, Vol. 5 ›› Issue (3): 230-235.

从传统到深度学习的图像隐写技术研究

周琳娜¹,曹洋²

1. 国际关系学院信息科技学院
2. 国际关系学院

收稿日期:2019-03-12 出版日期:2020-03-15 发布日期:2019-03-12
通讯作者: 周琳娜
作者简介:周琳娜教授，主要研究方向为大数据行为分析、信息隐藏、多媒体内容取证. zhoulinna@tsinghua.edu.cn 曹洋硕士研究生，主要研究方向为信息隐藏、网络与信息安全. caoyang_uir@sina.com

Image Steganography Methods from Traditional to Deep Learning

Received:2019-03-12 Online:2020-03-15 Published:2019-03-12

摘要/Abstract

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.

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

Abstract: 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.

Key words: image steganography, embedded steganography, steganography without embedding, deep learning, generative adversarial networks

周琳娜曹洋. 从传统到深度学习的图像隐写技术研究[J]. 信息安全研究, 2019, 5(3): 230-235.

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