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A Novel Image Encryption Algorithm Based on a Fractional-Order Hyperchaotic System and DNA Computing

Author

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  • Taiyong Li
  • Minggao Yang
  • Jiang Wu
  • Xin Jing

Abstract

In the era of the Internet, image encryption plays an important role in information security. Chaotic systems and DNA operations have been proven to be powerful for image encryption. To further enhance the security of image, in this paper, we propose a novel algorithm that combines the fractional-order hyperchaotic Lorenz system and DNA computing (FOHCLDNA) for image encryption. Specifically, the algorithm consists of four parts: firstly, we use a fractional-order hyperchaotic Lorenz system to generate a pseudorandom sequence that will be utilized during the whole encryption process; secondly, a simple but effective diffusion scheme is performed to spread the little change in one pixel to all the other pixels; thirdly, the plain image is encoded by DNA rules and corresponding DNA operations are performed; finally, global permutation and 2D and 3D permutation are performed on pixels, bits, and acid bases. The extensive experimental results on eight publicly available testing images demonstrate that the encryption algorithm can achieve state-of-the-art performance in terms of security and robustness when compared with some existing methods, showing that the FOHCLDNA is promising for image encryption.

Suggested Citation

  • Taiyong Li & Minggao Yang & Jiang Wu & Xin Jing, 2017. "A Novel Image Encryption Algorithm Based on a Fractional-Order Hyperchaotic System and DNA Computing," Complexity, Hindawi, vol. 2017, pages 1-13, November.
  • Handle: RePEc:hin:complx:9010251
    DOI: 10.1155/2017/9010251
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    Cited by:

    1. Guohui Li & Xiangyu Zhang & Hong Yang, 2019. "Numerical Analysis, Circuit Simulation, and Control Synchronization of Fractional-Order Unified Chaotic System," Mathematics, MDPI, vol. 7(11), pages 1-18, November.
    2. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    3. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.

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