新的基于双混沌系统和压缩感知的图像加密算法

doi:10.11959/j.issn.2096-109x.2022058

摘要/Abstract

摘要：

图像加密技术在当今多媒体应用和互联网信息安全传输中发挥着重要作用，但存在一些问题。多数图像加密算法存在密文图像在传输过程中占用带宽较高、图像加密速度慢、图像加密算法没有与明文关联、没有完备密文反馈机制等问题，这些问题影响着图像加密算法的安全性和易用性。为了解决上述问题，对压缩感知技术和混沌系统进行了研究，提出了一种新的基于双混沌系统和压缩感知的明文关联的图像加密算法。采用明文图像关联密钥，将明文图像哈希值与Logistic混沌系统参数进行关联；采用离散小波变换对明文图像进行稀疏处理；通过Logistic混沌系统生成随机测量矩阵，结合压缩感知技术和随机测量矩阵对图像进行一次加密，得到中间图像；对中间图像再次进行哈希，与明文图像哈希值共同关联 Rucklidge 混沌系统初值；使用 Rucklidge 混沌系统和加密算法控制中间图像进行二次加密，得到密文图像。加密算法是一种新的明文关联加密算法，该算法使用图像自身像素值控制中间图像置乱，可以增强明文关联，建立密文反馈机制。仿真结果和性能分析表明，所提算法加密性能良好，加密后图像可按照压缩比例进行压缩，有效减小密文图像尺寸，且可以很好地抵抗已知明文攻击、选择明文攻击、差分攻击等常见攻击，优于其他常见图像加密算法。

关键词: 图像加密, 压缩感知, 双混沌系统, 明文关联, 离散小波变换

Abstract:

Image encryption technology plays an important role in today’s multimedia applications and Internet information security transmission.However, most image encryption algorithms still have problems, for example, cipher images occupied high bandwidth during transmission, image encryption speed is slow, image encryption algorithms are not associated with plain image, and there is no complete ciphertext feedback mechanism.These problems all affect the security and easy use of image encryption algorithms.In order to solve the above problems, the compressive sensing technology and chaotic system were studied, and a new image encryption algorithm based on double chaotic system and compressive sensing with plaintext association was proposed.The plaintext image association key was used to associate the plain image hash value with the Logistic chaotic system parameters.Discrete wavelet transform, was used to sparse the plain image.Then the random measurement matrix was generated by the Logistic chaos system, and the image was encrypted once by combining the compressive sensing technique and the random measurement matrix to obtain the intermediate image.The intermediate image was hashed again, and the Rucklidge chaos system initial value was associated with the plain image hash value together.The Rucklidge chaotic system and encryption algorithm were used to control the intermediate image for secondary encryption and accordingly obtain the cipher image.The encryption algorithm was a new plaintext correlation encryption algorithm, which used the image’s own pixel value to control the scrambling of the intermediate image.It also enhanced the plaintext association and established a ciphertext feedback mechanism.Simulation results and performance analysis show that the algorithm has good encryption performance.The encrypted image can be compressed according to the compression ratio, effectively reducing the size of the cipher image.Moreover, it is resistant to common attacks such as known plaintext attack, selective plaintext attack and differential attack, which is better than other common image encryption algorithms.

Key words: image encryption, compressive sensing, double chaotic system, plaintext association, discrete wavelet transform

中图分类号:

TP309

杨宇光, 曹国栋. 新的基于双混沌系统和压缩感知的图像加密算法[J]. 网络与信息安全学报, 2022, 8(5): 88-97.

Yuguang YANG, Guodong CAO. New image encryption algorithm based on double chaotic system and compressive sensing[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 88-97.

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图像		明文图像			密文图像
图像	水平方向	垂直方向	对角线方向	水平方向	垂直方向	对角线方向
Lena	0.972 1	0.984 7	0.956 8	0.001	-0.002 4	0.000 8
Cameramen	0.982 4	0.989 6	0.972 2	-0.000 2	0.005 2	-0.000 8
House	0.995 6	0.994 5	0.990 4	-0.003 3	0.005 2	-0.000 7
Jetplane	0.972 7	0.970 3	0.948 7	0.007 4	0.001 1	-0.004 5
Lake	0.975 9	0.976 4	0.961 2	-0.001 6	0.000 3	-0.005 5
Livingroom	0.946 3	0.952 9	0.912 0	0.000 3	0.000 9	-0.007 4
Woman	0.996 4	0.996 9	0.994 5	-0.001	-0.002 4	-0.001 1
Peppers	0.980 4	0.980 3	0.970 6	-0.001 4	-0.003 4	0.007 7
Pirate	0.977 5	0.981 3	0.967 0	-0.008 5	-0.001 5	-0.003 5
Walkbridge	0.941 6	0.939 4	0.904 8	0.000 2	-0.002 4	0.001 3
均值	0.972 4	0.974 5	0.955 5	-0.000 71	0.000 06	-0.001 37

算法		相关系数
算法	水平方向	垂直方向	对角线方向
文献[17]	0.002	-0.000 7	-0.001 4
文献[18]	-0.001 7	-0.013 2	0.008 4
文献[19]	0.013 7	0.000 217 8	0.000 537
文献[20]	0.002 6	-0.003 8	0.006 2
本文	-0.000 71	0.000 06	-0.001 37

图像	明文图像信息熵/bit	密文图像信息熵/bit
Lena	7.445	7.998
Cameramen	7.048	7.998
House	5.752	7.998
Jetplane	6.713	7.998
Lake	7.482	7.998

算法	信息熵/bit
文献[17]	7.997 2
文献[18]	7.997 2
文献[19]	7.999 3
文献[20]	7.998 3
本文	7.998 7

CR	Lena	Cameramen	House	Jetplane	Lake
0.25	24.73	26.663 3	38.160 9	31.426 2	25.423 2
0.50	34.6481	31.839 1	38.002 4	34.711 1	32.21 8
0.75	34.6526	33.109 8	37.990 0	34.707 5	32.159 6