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Color image encryption based on Coupled Nonlinear Chaotic Map

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  • Mazloom, Sahar
  • Eftekhari-Moghadam, Amir Masud

Abstract

Image encryption is somehow different from text encryption due to some inherent features of image such as bulk data capacity and high correlation among pixels, which are generally difficult to handle by conventional methods. The desirable cryptographic properties of the chaotic maps such as sensitivity to initial conditions and random-like behavior have attracted the attention of cryptographers to develop new encryption algorithms. Therefore, recent researches of image encryption algorithms have been increasingly based on chaotic systems, though the drawbacks of small key space and weak security in one-dimensional chaotic cryptosystems are obvious. This paper proposes a Coupled Nonlinear Chaotic Map, called CNCM, and a novel chaos-based image encryption algorithm to encrypt color images by using CNCM. The chaotic cryptography technique which used in this paper is a symmetric key cryptography with a stream cipher structure. In order to increase the security of the proposed algorithm, 240bit-long secret key is used to generate the initial conditions and parameters of the chaotic map by making some algebraic transformations to the key. These transformations as well as the nonlinearity and coupling structure of the CNCM have enhanced the cryptosystem security. For getting higher security and higher complexity, the current paper employs the image size and color components to cryptosystem, thereby significantly increasing the resistance to known/chosen-plaintext attacks. The results of several experimental, statistical analysis and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for real-time image encryption and transmission.

Suggested Citation

  • Mazloom, Sahar & Eftekhari-Moghadam, Amir Masud, 2009. "Color image encryption based on Coupled Nonlinear Chaotic Map," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1745-1754.
  • Handle: RePEc:eee:chsofr:v:42:y:2009:i:3:p:1745-1754
    DOI: 10.1016/j.chaos.2009.03.084
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    References listed on IDEAS

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    8. Rhouma, Rhouma & Meherzi, Soumaya & Belghith, Safya, 2009. "OCML-based colour image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 40(1), pages 309-318.
    9. Gao, Tiegang & Chen, Zengqiang, 2008. "Image encryption based on a new total shuffling algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 38(1), pages 213-220.
    10. Behnia, S. & Akhshani, A. & Mahmodi, H. & Akhavan, A., 2008. "A novel algorithm for image encryption based on mixture of chaotic maps," Chaos, Solitons & Fractals, Elsevier, vol. 35(2), pages 408-419.
    11. Arroyo, David & Li, Chengqing & Li, Shujun & Alvarez, Gonzalo & Halang, Wolfgang A., 2009. "Cryptanalysis of an image encryption scheme based on a new total shuffling algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2613-2616.
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    Cited by:

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    2. Bowen Zhang & Lingfeng Liu, 2023. "Chaos-Based Image Encryption: Review, Application, and Challenges," Mathematics, MDPI, vol. 11(11), pages 1-39, June.
    3. Othman Abdullah Almatroud & Viet-Thanh Pham, 2023. "Building Fixed Point-Free Maps with Memristor," Mathematics, MDPI, vol. 11(6), pages 1-11, March.
    4. Lu, Guangqing & Smidtaite, Rasa & Navickas, Zenonas & Ragulskis, Minvydas, 2018. "The Effect of Explosive Divergence in a Coupled Map Lattice of Matrices," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 308-313.
    5. Wenqiang Yang & Li Xiao & Junjian Huang & Jinyue Yang, 2021. "Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection," Mathematics, MDPI, vol. 9(23), pages 1-14, November.
    6. Wang, Mingxu & Wang, Xingyuan & Wang, Chunpeng & Xia, Zhiqiu & Zhao, Hongyu & Gao, Suo & Zhou, Shuang & Yao, Nianmin, 2020. "Spatiotemporal chaos in cross coupled map lattice with dynamic coupling coefficient and its application in bit-level color image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Zhang, Ying-Qian & Wang, Xing-Yuan, 2014. "Spatiotemporal chaos in mixed linear–nonlinear coupled logistic map lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 104-118.
    8. Wang, Xingyuan & Yang, Jingjing & Guan, Nana, 2021. "High-sensitivity image encryption algorithm with random cross diffusion based on dynamically random coupled map lattice model," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    9. Nada E. El-Meligy & Tamer O. Diab & Ashraf S. Mohra & Ashraf Y. Hassan & Wageda I. El-Sobky, 2022. "A Novel Dynamic Mathematical Model Applied in Hash Function Based on DNA Algorithm and Chaotic Maps," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    10. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

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