基于Henon映射与改进的提升小波变换图像加密算法

doi:10.3969/j.issn.1671-1122.2022.09.004

信息网络安全 ›› 2022, Vol. 22 ›› Issue (9): 31-39.doi: 10.3969/j.issn.1671-1122.2022.09.004

基于Henon映射与改进的提升小波变换图像加密算法

佟晓筠¹, 毛宁¹(), 张淼¹, 王翥²

1.哈尔滨工业大学（威海）计算机科学与技术学院，威海 264209
2.哈尔滨工业大学（威海）信息科学与工程学院，威海 264209

收稿日期:2022-06-01 出版日期:2022-09-10 发布日期:2022-11-14
通讯作者: 毛宁 E-mail:maoninggirl@163.com
作者简介:佟晓筠（1963—），女，辽宁，教授，博士，主要研究方向为信息安全|毛宁（1999—），女，黑龙江，硕士研究生，主要研究方向为信息安全|张淼（1979—），女，内蒙古，教授，博士，主要研究方向为混沌密码学|王翥（1963—），男，辽宁，教授，博士，主要研究方向为无线传感器网络与安全
基金资助:
国家自然科学基金(61902091);山东省自然科学基金(ZR2019MF054)

Image Encryption Algorithm Based on Henon Mapping and Improved Lifting Wavelet Transform

TONG Xiaojun¹, MAO Ning¹(), ZHANG Miao¹, WANG Zhu²

1. Department of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
2. Department of Information science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China

Received:2022-06-01 Online:2022-09-10 Published:2022-11-14
Contact: MAO Ning E-mail:maoninggirl@163.com

摘要/Abstract

摘要：

数据在开放的网络环境中传输容易被盗取或破坏，因此常常利用混沌系统结合各种算法对多媒体数据进行加密。针对传统置乱扩散结构安全性低、依赖大量伪随机数的问题，文章提出一种基于Henon映射和改进的提升小波变换图像加密算法。首先，该算法对混沌系统产生的伪随机序列进行循环移位以及S盒处理生成密钥，同时将普通明文图像变换成一维奇偶索引序列;其次，将变换后的序列进行预测和更新处理，得到图像的低频近似分量和高频细节分量，完成一次加密;最后，分别翻转这两个被扰乱的分量，并再次执行预测和更新算法，完成对图像的加密。灰度图像上的计算机仿真实验与性能分析表明，使用该算法加密的图像失去了明显的统计特性，可以抵抗统计性攻击，有效提高了图像加密的安全性。

关键词: Henon映射, 提升小波变换, 伪随机序列, 图像加密

Abstract:

Data transmission in an open network environment is easily stolen or destroyed, therefore, chaotic systems are often used to encrypt multimedia data in combination with various algorithms. Aiming at the problems of traditional scrambling diffusion structure’s low security and depending on a large number of pseudo-random numbers, an image encryption algorithm based on Henon map and improved lifting wavelet transform was proposed. Firstly, the algorithm generated the key by cyclic shift and S-box processing of the pseudo-random sequence generated by chaotic system. At the same time, the plain image was processed to obtain one-dimensional parity sequences. Secondly, the transformed sequence was predicted and updated to get the low-frequency approximate component and the high-frequency detail component of the image. In this way, the primary encryption was completed. Finally, the two scrambled components were flipped separately and the same operation was performed again to achieve encryption. Computer simulation experiment and performance analysis on gray image show that the image encrypted by this algorithm loses obvious statistical characteristics and can resist statistical attacks, thus effectively improve the security of image encryption.

Key words: Henon mapping, lifting wavelet transform, pseudo-random sequences, image encryption

中图分类号:

TP309

佟晓筠, 毛宁, 张淼, 王翥. 基于Henon映射与改进的提升小波变换图像加密算法[J]. 信息网络安全, 2022, 22(9): 31-39.

TONG Xiaojun, MAO Ning, ZHANG Miao, WANG Zhu. Image Encryption Algorithm Based on Henon Mapping and Improved Lifting Wavelet Transform[J]. Netinfo Security, 2022, 22(9): 31-39.

图/表 15

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参考文献 23

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	本文测试值	理论值
NPCR	99.6307%	99.6094%
UACI	33.4434%	33.4635%
BACI	26.7998%	26.7712%

	Baboon	Female	House
明文图像	7.0092	7.2575	6.4961
密文图像	7.9970	7.9970	7.9971

测试图像		水平方向	垂直方向	对角线方向
Baboon	明文图像	0.8278	0.8773	0.7860
Baboon	密文图像	0.0028	0.0094	-0.0004
Female	明文图像	0.9867	0.9726	0.9605
Female	密文图像	0.0015	-0.0124	0.0089
House	明文图像	0.9537	0.9802	0.9830
House	密文图像	-0.0096	-0.0093	0.0056

测试图像	方向	本文算法	文献[23] 方法	文献[21] 方法	文献[22] 方法
Lena	水平方向	-0.0010	0.0065	0.0036	-0.0223
	垂直方向	-0.0038	0.0035	0.0023	-0.0084
	对角线方向	-0.0134	0.0036	0.0039	-0.0086

测试图像	信息熵	相关性数值
测试图像	信息熵	水平方向	垂直方向	对角线方向
全黑	7.9786	-0.0400	-0.0032	0.0032
全白	7.9797	-0.0309	-0.0148	-0.0009

基于Henon映射与改进的提升小波变换图像加密算法

Image Encryption Algorithm Based on Henon Mapping and Improved Lifting Wavelet Transform

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