Nonlinear Dynamics

, Volume 83, Issue 1–2, pp 333–346 | Cite as

A novel image encryption algorithm based on genetic recombination and hyper-chaotic systems

  • Xingyuan Wang
  • Hui-li Zhang
Original Paper


In this paper, a novel image encryption algorithm based on genetic recombination and hyper-chaotic system is proposed. The basic rules of genetic recombination are employed to scramble images because of its effectiveness. Specifically, the plain image is expanded into two compound images composed of selected four bit-planes and diffuse them at bit-plane level, the compound bit-planes and key streams are reconstructed based on the principles of genetic recombination, then perform traditional diffusion and obtain cipher images. The hyper-chaotic Lorenz system in this algorithm generates pseudorandom sequences in each phase. The experiment results and analysis have proved that the novel image encryption algorithm is effective for image encryption.


Image encryption Genetic recombination Bit-plane level Hyper-chaos 



This research is supported by the National Natural Science Foundation of China (Nos. 61370145, 61173183 and 60973152), the Doctoral Program Foundation of Institution of Higher Education of China (No. 20070141014), Program for Liaoning Excellent Talents in University (No. LR2012003), the National Natural Science Foundation of Liaoning Province (No. 20082165) and the Fundamental Research Funds for the Central Universities (No. DUT12JB06).


  1. 1.
    Wang, Y., Wong, K.W., Liao, X.F., Xiang, T., Chen, G.R.: A chaos-based image encryption algorithm with variable control parameters. Chaos Solitons Fractals 41(4), 1773–1783 (2009)CrossRefMATHGoogle Scholar
  2. 2.
    Niknam, T., Khooban, M.H.: Fuzzy sliding mode control scheme for a class of non-linear uncertain chaotic systems. IET Sci. Meas. Technol. 7(5), 249–255 (2013)CrossRefGoogle Scholar
  3. 3.
    Alfi, A., Kalat, A.A., Khooban, M.H.: Adaptive fuzzy sliding mode control for synchronization of uncertain non-identical chaotic systems using bacterial foraging optimization. J. Intell. Fuzzy Syst. 26(5), 2567–2576 (2014)MathSciNetMATHGoogle Scholar
  4. 4.
    Khooban, M.H., Alfi, A., Abadi, D.N.M.: Control of a class of non-linear uncertain chaotic systems via an optimal Type-2 fuzzy proportional integral derivative controller. IET Sci. Meas. Technol. 7(1), 50–58 (2013)CrossRefGoogle Scholar
  5. 5.
    Liu, B., Peng, J.: Nonlinear Dynamics. High Education Press, Beijing (2004)Google Scholar
  6. 6.
    Liu, H.J., Wang, X.Y.: Color image encryption based on one-time keys and robust chaotic maps. Comput. Math. Appl. 59(10), 3320–3327 (2010)CrossRefMathSciNetMATHGoogle Scholar
  7. 7.
    Zhou, Y.C., Bao, L., Chen, C.L.P.: Image encryption using a new parametric switching chaotic system. Signal Process. 93(11), 3039–3052 (2013)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y.S., Xiao, D.: An image encryption scheme based on rotation matrix bit-level permutation and block diffusion. Commun. Nonlin. Sci. Numer. Simul. 19(1), 74–82 (2014)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Zhang, W., Wong, K.W., Yu, H.: A symmetric color image encryption algorithm using the intrinsic features of bit distributions. Commun. Nonlin. Sci. Numer. Simul. 18(3), 584–600 (2013)CrossRefMathSciNetMATHGoogle Scholar
  10. 10.
    Wang, X.Y., Qin, X.: A new pseudo-random number generator based on CML and chaotic iteration. Nonlin. Dyn. 70(2), 1589–1592 (2012)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Wang, X.Y., Teng, L.: An image blocks encryption algorithm based on spatiotemporal chaos. Nonlin. Dyn. 67(1), 365–371 (2012)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Tang, Y., Wang, Z.D., Fang, J.A.: Image encryption using chaotic coupled map lattices with time-varying delays. Commun. Nonlin. Sci. Numer. Simul. 15(9), 2456–2468 (2010)CrossRefMathSciNetMATHGoogle Scholar
  13. 13.
    Mao, Y.B., Chen, G.R., Lian, S.G.: A novel fast image encryption based on 3D chaotic baker maps. Int. J. Bifurc. Chaos 14(10), 3613–3624 (2004)CrossRefMathSciNetMATHGoogle Scholar
  14. 14.
    Gao, T.G., Chen, Z.Q.: A new image encryption algorithm based on hyper-chaos. Phys. Lett. A 372(4), 394–400 (2008)CrossRefMathSciNetMATHGoogle Scholar
  15. 15.
    Rhouma, R., Belghith, S.: Cryptanalysis of a new image encryption algorithm based on hyper-chaos. Phys. Lett. A 372(38), 5973–5978 (2008)CrossRefMATHGoogle Scholar
  16. 16.
    Jeng, F.G., Huang, W.L., Chen, T.H.: Cryptanalysis and improvement of two hyper-chaos-based image encryption schemes. Signal Process. Image Commun. 34, 45–51 (2015)CrossRefGoogle Scholar
  17. 17.
    Watson, J.D., Crick, F.H.C.: A structure for deoxyribose nucleic acid. Nature 248(5451), 737–738 (1953)CrossRefGoogle Scholar
  18. 18.
    Leier, A., Richter, C., Banzhaf, W.: Cryptography with DNA binary strands. Biosystems 57(1), 13–22 (2000)CrossRefGoogle Scholar
  19. 19.
    Wang, X.Y., Zhang, Y.Q., Bao, X.M.: A novel chaotic image encryption scheme using DNA sequence operations. Opt. Lasers Eng. 73, 53–61 (2015)CrossRefGoogle Scholar
  20. 20.
    Liu, H.J., Wang, X.Y., Kadir, A.: Image encryption using DNA complementary rule and chaotic maps. Appl. Soft Comput. 12(5), 1457–1466 (2012)CrossRefGoogle Scholar
  21. 21.
    Morteza, S., Dzulkifli, M., Mohd, R.M.S.: Using 3-cell chaotic map for image encryption based on biological operations. Nonlin. Dyn. 75(3), 407–416 (2014)CrossRefGoogle Scholar
  22. 22.
    Xue, X.L., Zhang, Q., Wei, X.P.: An image fusion encryption algorithm based on DNA sequence and multi-chaotic maps. J. Comput. Theor. Nanosci. 7(2), 397–403 (2010)CrossRefGoogle Scholar
  23. 23.
    Zhang, Y.S., Wen, W.Y., Su, M.T.: Cryptanalyzing a novel image fusion encryption algorithm based on DNA sequence operation and hyper-chaotic system. Optik 125(4), 1562–1564 (2014)CrossRefGoogle Scholar
  24. 24.
    Wang, X.Y., Wang, M.J.: A hyperchaos generated from Lorenz system. Phys. A 387(14), 3751–3758 (2008)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Ramasubramanian, K., Sriram, M.S.: A comparative study of computation of Lyapunov spectra with different algorithms. Phys. D 139(1–2), 72–86 (2000)Google Scholar
  26. 26.
    Medasani, S., Kim, J., Krishnapuram, R.: An overview of membership function generation techniques for pattern recognition. Int. J. Approx. Reason. 19(3–4), 391–417 (1998)CrossRefMathSciNetMATHGoogle Scholar
  27. 27.
    Zhu, Z.L., Zhang, W., Wong, K.W., Yu, H.: A chaos-based symmetric image encryption scheme using a bit-level permutation. Inf. Sci. 181(6), 1171–1186 (2011)CrossRefGoogle Scholar
  28. 28.
    Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E., Leigh, S., Levenson, M., Vangel, M., Banks, D., Heckert, A., Dray, J., Vo, S.: A Statistical Test Suite for the Validation of Random Number Generators and Pseudorandom Number Generators for Cryptographic Applications. NIST Special Publication 800-22, Gaithersburg, MD (2010)Google Scholar
  29. 29.
    Chen, G.R., Mao, Y., Chui, C.K.: A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Solitons Fractals 21(3), 749–761 (2004)CrossRefMathSciNetMATHGoogle Scholar
  30. 30.
    Wang, Y., Wong, K.W., Liao, X.F., Chen, G.R.: A new chaos-based fast image encryption algorithm. Appl. Soft Comput. 11(1), 514–522 (2011)CrossRefGoogle Scholar
  31. 31.
    Wong, K.W., Kwok, B.S., Law, W.S.: A fast image encryption scheme based on chaotic standard map. Phys. Lett. A 372, 2645–2652 (2008)CrossRefMATHGoogle Scholar
  32. 32.
    Benyamin, N., Mohammad, S.S., Sattar, M.: A novel image encryption based on hash function with only two-round diffusion process. Multimed. Syst. 20(1), 45–64 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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