Full Text:   <3949>

Summary:  <226>

Suppl. Mater.: 

CLC number: TN919.81

On-line Access: 2023-07-03

Received: 2022-12-16

Revision Accepted: 2023-07-03

Crosschecked: 2023-03-02

Cited: 0

Clicked: 1040

Citations:  Bibtex RefMan EndNote GB/T7714


Yuanyuan LI


Jianquan LU


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.6 P.813-827


A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations

Author(s):  Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU

Affiliation(s):  Department of Applied Mathematics, College of Science, Nanjing Forestry University, Nanjing 210037, China; more

Corresponding email(s):   jqluma@seu.edu.cn

Key Words:  Compressive sensing, Coupled map lattice (CML), DNA operations, Semi-tensor product

Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU. A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(6): 813-827.

@article{title="A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations",
author="Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations
%A Yuanyuan LI
%A Xiaoqing YOU
%A Jianquan LU
%A Jungang LOU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 6
%P 813-827
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200645

T1 - A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations
A1 - Yuanyuan LI
A1 - Xiaoqing YOU
A1 - Jianquan LU
A1 - Jungang LOU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 6
SP - 813
EP - 827
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200645

In this paper, an efficient image encryption scheme based on a novel mixed linear–nonlinear coupled map lattice (NMLNCML) system and DNA operations is presented. The proposed NMLNCML system strengthens the chaotic characteristics of the system, and is applicable for image encryption. The main advantages of the proposed method are embodied in its extensive key space; high sensitivity to secret keys; great resistance to chosen-plaintext attack, statistical attack, and differential attack; and good robustness to noise and data loss. Our image cryptosystem adopts the architecture of scrambling, compression, and diffusion. First, a plain image is transformed to a sparsity coefficient matrix by discrete wavelet transform, and plaintext-related Arnold scrambling is performed on the coefficient matrix. Then, semi-tensor product (STP) compressive sensing is employed to compress and encrypt the coefficient matrix. Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit XOR operation. The NMLNCML system is applied to generate chaotic elements in the STP measurement matrix of compressive sensing and the pseudo-random sequence in DNA operations. An SHA-384 function is used to produce plaintext secret keys and thus makes the proposed encryption algorithm highly sensitive to the original image. Simulation results and performance analyses verify the security and effectiveness of our scheme.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Alvarez G, Li SJ, 2006. Some basic cryptographic requirements for chaos-based cryptosystems. Int J Bifurc Chaos, 16(8):2129-2151.

[2]Chai XL, Chen YR, Broyde L, 2017. A novel chaos-based image encryption algorithm using DNA sequence operations. Opt Lasers Eng, 88:197-213.

[3]Chai XL, Fu XL, Gan ZH, et al., 2020a. An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata. Neur Comput Appl, 32(9):4961-4988.

[4]Chai XL, Wu HY, Gan ZH, et al., 2020b. An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt Lasers Eng, 124:105837.

[5]Chai XL, Fu JY, Gan ZH, et al., 2022a. An image encryption scheme based on multi-objective optimization and block compressed sensing. Nonl Dyn, 108(3):2671-2704.

[6]Chai XL, Wang YJ, Chen XH, et al., 2022b. TPE-GAN: thumbnail preserving encryption based on GAN with key. IEEE Signal Process Lett, 29:972-976.

[7]Chen JX, Zhu ZL, Zhang LB, et al., 2018. Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption. Signal Process, 142:340-353.

[8]Chen L, Li CQ, Li C, 2022. Security measurement of a medical communication scheme based on chaos and DNA coding. J Vis Commun Image Represent, 83:103424.

[9]Chen TH, Zhang M, Wu JH, et al., 2016. Image encryption and compression based on kronecker compressed sensing and elementary cellular automata scrambling. Opt Laser Technol, 84:118-133.

[10]Donoho DL, 2006. Compressed sensing. IEEE Trans Inform Theory, 52(4):1289-1306.

[11]Feng W, He YG, Li HM, et al., 2019. A plain-image-related chaotic image encryption algorithm based on DNA sequence operation and discrete logarithm. IEEE Access, 7:181589-181609.

[12]Fira M, 2015. Applications of compressed sensing: compression and encryption. Health and Bioengineering Conf, p.1-4.

[13]Gan ZH, Chai XL, Zhang JT, et al., 2020. An effective image compression-encryption scheme based on compressive sensing (CS) and game of life (GOL). Neur Comput Appl, 32(17):14113-14141.

[14]Guo SF, Liu Y, Gong LH, et al., 2018. Bit-level image cryptosystem combining 2D hyper-chaos with a modified non-adjacent spatiotemporal chaos. Multimed Tools Appl, 77(16):21109-21130.

[15]Hu GQ, Xiao D, Wang Y, et al., 2017. An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image Represent, 44:116-127.

[16]Hu HH, Liu JD, Shang K, et al., 2018. Parallel image encryption algorithm based on integer chaos and DNA coding. Comput Eng Des, 39(8):2401-2406 (in Chinese).

[17]Kafedziski V, Stojanovski T, 2011. Compressive sampling with chaotic dynamical systems. 19th Telecommunications Forum, p.695-698.

[18]Kaneko K, 1993. Theory and Applications of Coupled Map Lattices. John Wiley & Sons, Hoboken, USA.

[19]Li LX, Liu LF, Peng HP, et al., 2019. Flexible and secure data transmission system based on semitensor compressive sensing in wireless body area networks. IEEE Int Things J, 6:3212-3227.

[20]Li LX, Wen GQ, Wang ZM, et al., 2020. Efficient and secure image communication system based on compressed sensing for IoT monitoring applications. IEEE Trans Multimed, 22(1):82-95.

[21]Li XD, Song SJ, Wu JH, 2019. Exponential stability of nonlinear systems with delayed impulses and applications. IEEE Trans Autom Contr, 64(10):4024-4034.

[22]Li XD, Peng DX, Cao JD, 2020. Lyapunov stability for impulsive systems via event-triggered impulsive control. IEEE Trans Autom Contr, 65(11):4908-4913.

[23]Li XH, Zhou LL, Tan F, 2022. An image encryption scheme based on finite-time cluster synchronization of two-layer complex dynamic networks. Soft Comput, 26(2):511-525.

[24]Lu JQ, Sun LJ, Liu Y, et al., 2018. Stabilization of Boolean control networks under aperiodic sampled-data control. SIAM J Contr Optim, 56(6):4385-4404.

[25]Lu JQ, Li BW, Zhong J, 2021. A novel synthesis method for reliable feedback shift registers via Boolean networks. Sci China Inform Sci, 64(5):152207.

[26]Peng YX, He SB, Sun KH, 2021. A higher dimensional chaotic map with discrete memristor. Int J Electron Commun, 129:153539.

[27]Rani M, Dhok SB, Deshmukh RB, 2018 A systematic review of compressive sensing: concepts, implementations and applications. IEEE Access, 6:4875-4894.

[28]Shao WD, Cheng MF, Luo CK, et al., 2019. An image encryption scheme based on hybrid electro-optic chaotic sources and compressive sensing. IEEE Access, 7:156582-156591.

[29]Song CY, Qiao YL, 2015. A novel image encryption algorithm based on DNA encoding and spatiotemporal chaos. Entropy, 17(10):6954-6968.

[30]Sreedhanya AV, Soman KP, 2012. Secrecy of cryptography with compressed sensing. Int Conf on Advances in Computing and Communications, p.207-210.

[31]Testa M, Bianchi T, Magli E, 2020. Secrecy analysis of finite-precision compressive cryptosystems. IEEE Trans Inform Forens Secur, 15:1-13.

[32]Wang XY, Wang T, 2012. A novel algorithm for image encryption based on couple chaotic systems. Int J Mod Phys B, 26(30):1250175.

[33]Wang XY, Liu PB, 2020. A new image encryption scheme based on a novel one-dimensional chaotic system. IEEE Access, 8:174463-174479.

[34]Wang Y, Wong KW, Liao XF, et al., 2011. A new chaos-based fast image encryption algorithm. Appl Soft Comput, 11(1):514-522.

[35]Wen WY, Hong YK, Fang YM, et al., 2020. A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Process, 173:107580.

[36]Wu CW, Wu LG, Liu JX, et al., 2020. Active defense-based resilient sliding mode control under denial-of-service attacks. IEEE Trans Inform Forens Secur, 15:237-249.

[37]Wu Y, Noonan JP, Agaian S, 2011. NPCR and UACI randomness tests for image encryption. J Sel Areas Telecommun, April Edition, p.31-38.

[38]Xie D, Peng HP, Li LX, et al., 2016. Semi-tensor compressed sensing. Dig Signal Process, 58:85-92.

[39]Xu H, Tong XJ, Zhang M, et al., 2016. Dynamic video encryption algorithm for H.264/AVC based on a spatiotemporal chaos system. J Opt Soc Am A, 33(6):1166-1174.

[40]Zhang LY, Liu YS, Pareschi F, et al., 2018. On the security of a class of diffusion mechanisms for image encryption. IEEE Trans Cybern, 48(4):1163-1175.

[41]Zhang YQ, Wang XY, 2014. Spatiotemporal chaos in mixed linear-nonlinear coupled logistic map lattice. Phys A Stat Mech Appl, 402:104-118.

[42]Zhang YQ, Wang XY, Liu J, et al., 2016. An image encryption scheme based on the MLNCML system using DNA sequences. Opt Lasers Eng, 82:95-103.

[43]Zhong YS, Xu X, 2015. A novel image encryption method based on couple mapped lattice and two-stage diffusion. Int J Secur Appl, 9(11):281-292.

[44]Zhou SW, He Y, Liu YH, et al., 2021. Multi-channel deep networks for block-based image compressive sensing. IEEE Trans Multimed, 23:2627-2640.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE