Skip to main content
SpringerLink
Log in

Adaptive Differential Evolution-Based Lorenz Chaotic System for Image Encryption

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The main challenge for Lorenz chaotic system-based image encryption techniques is parameter sensitivity and resistance against attacks. To resolve these issues, a modified image encryption technique based on secure hash algorithm (SHA-3) and adaptive differential evolution (ADE) is proposed. In the proposed technique, ADE is used to optimize the input parameters of Lorenz chaotic system. SHA-3 is used to generate secret key based on the input image. The optimized parameters and external secret keys are used to generate initial values for Lorenz chaotic system that make it sensitive toward input image and provide resistance against both known-plaintext and known-ciphertext attacks. The proposed technique is compared with five well-known image encryption techniques over four color images. The experimental results reveal that the proposed technique outperforms existing techniques in terms of security and quality measures. The noise and enhancement attacks are also applied to test the robustness of proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad, M.; Farooq, O.: Secure satellite images transmission scheme based on chaos and discrete wavelet transform. In: Mantri, A., Nandi, S., Kumar, G., Kumar, S. (eds.) High Performance Architecture and Grid Computing, pp. 257–264. Springer, Berlin, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Shannon, C.E.: Communication theory of secrecy systems. Bell Labs Tech. J. 28(4), 656–715 (1949)

    Article  MathSciNet  Google Scholar 

  3. Norouzi, B.; Mirzakuchaki, S.; Seyedzadeh, S.M.; Mosavi, M.R.: A simple, sensitive and secure image encryption algorithm based on hyper-chaotic system with only one round diffusion process. Multimed. Tools Appl. 71(3), 1469–1497 (2014)

    Article  Google Scholar 

  4. Usama, M.; Khan, M.K.; Alghathbar, K.; Lee, C.: Chaos-based secure satellite imagery cryptosystem. Comput. Math. Appl. 60(2), 326–337 (2010)

    Article  MathSciNet  Google Scholar 

  5. Wen, W.: Security analysis of a color image encryption scheme based on skew tent map and hyper chaotic system of 6th-order cnn against chosen-plaintext attack. Multimed. Tools Appl. 75(6), 3553–3560 (2016)

    Article  Google Scholar 

  6. Zhang, X.; Zhu, G.; Ma, S.: Remote-sensing image encryption in hybrid domains. Opt. Commun. 285(7), 1736–1743 (2012)

    Article  Google Scholar 

  7. Wang, X.; Yin, Y.L.; Yu, H.: Finding collisions in the full SHA-1. In: Crypto, vol. 3621, pp. 17–36. Springer, Berlin (2005)

  8. Muhaya, F.T.B.: Chaotic and aes cryptosystem for satellite imagery. Telecommun. Syst. 52, 1–9 (2013)

    Article  Google Scholar 

  9. Huang, X.; Ye, G.; Chai, H.; Xie, O.: Compression and encryption for remote sensing image using chaotic system. Secur. Commun. Netw. 8(18), 3659–3666 (2015)

    Article  Google Scholar 

  10. Liu, Y.; Zhang, L.Y.; Wang, J.; Zhang, Y.; Wong, K.-W.: Chosen-plaintext attack of an image encryption scheme based on modified permutation–diffusion structure. Nonlinear Dyn. 84(4), 2241–2250 (2016)

    Article  Google Scholar 

  11. Ye, G.; Huang, X.: A novel block chaotic encryption scheme for remote sensing image. Multimed. Tools Appl. 75(18), 11433–11446 (2016)

    Article  Google Scholar 

  12. Sreelaja, N.; Pai, G.V.: Stream cipher for binary image encryption using ant colony optimization based key generation. Appl. Soft Comput. 12(9), 2879–2895 (2012)

    Article  Google Scholar 

  13. Abdullah, A.H.; Enayatifar, R.; Lee, M.: A hybrid genetic algorithm and chaotic function model for image encryption. AEU Int. J. Electron. Commun. 66(10), 806–816 (2012)

    Article  Google Scholar 

  14. Enayatifar, R.; Abdullah, A.H.; Isnin, I.F.: Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence. Opt. Lasers Eng. 56, 83–93 (2014)

    Article  Google Scholar 

  15. Zhang, X.; Wang, X.; Cheng, Y.: Image encryption based on a genetic algorithm and a chaotic system. IEICE Trans. Commun. 98(5), 824–833 (2015)

    Article  Google Scholar 

  16. Enayatifar, R.; Abdullah, A.H.; Lee, M.: A weighted discrete imperialist competitive algorithm (wdica) combined with chaotic map for image encryption. Opt. Lasers Eng. 51(9), 1066–1077 (2013)

    Article  Google Scholar 

  17. Talarposhti, K.M.; Jamei, M.K.: A secure image encryption method based on dynamic harmony search (DHS) combined with chaotic map. Opt. Lasers Eng. 81, 21–34 (2016)

    Article  Google Scholar 

  18. Li, C.: Cracking a hierarchical chaotic image encryption algorithm based on permutation. Signal Process. 118, 203–210 (2016)

    Article  Google Scholar 

  19. Lorenz, E.N.: Deterministic nonperiodic flow. J Atmospheric Sci. 20, 130–141 (1963)

    Article  Google Scholar 

  20. Sharma, N.; Saini, I.; Yadav, A.; Singh, P.: Phase-image encryption based on 3D-Lorenz chaotic system and double random phase encoding. 3D Res. 8(4), 39 (2017)

    Article  Google Scholar 

  21. Huang, L.; Wang, X.; Sun, G.: Design and circuit simulation of the new Lorenz chaotic system. In: 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA), pp. 1443–1447. IEEE (2010)

  22. Huang, L.; Shi, D.; Gao, J.: The design and its application in secure communication and image encryption of a new Lorenz-like system with varying parameter. Math. Probl. Eng. 2016 (2016). https://doi.org/10.1155/2016/8973583

    MathSciNet  Google Scholar 

  23. Lin, Q.; Zhu, Q.; Huang, P.; Chen, J.; Ming, Z.; Yu, J.: A novel hybrid multi-objective immune algorithm with adaptive differential evolution. Comput. Oper. Res. 62, 95–111 (2015)

    Article  MathSciNet  Google Scholar 

  24. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  25. Lin, Q.; Chen, J.: A novel micro-population immune multiobjective optimization algorithm. Comput. Oper. Res. 40(6), 1590–1601 (2013)

    Article  MathSciNet  Google Scholar 

  26. Kaur, M.; Kumar, V.: An efficient image encryption method based on improved lorenz chaotic system. Electron. Lett. 54, 562–564 (2018)

    Article  Google Scholar 

  27. Kaur, M.; Kumar, V.: Fourier–Mellin moment-based intertwining map for image encryption. Mod. Phys. Lett. B 32, 1850115 (2018)

    Article  MathSciNet  Google Scholar 

  28. Kaur, M.; Kumar, V.: Color image encryption technique using differential evolution in non-subsampled contourlet transform domain. IET Image Process. (2018). https://doi.org/10.1049/iet-ipr.2017.1016

    Article  Google Scholar 

  29. Mondal, B.; Mandal, T.: A light weight secure image encryption scheme based on chaos & DNA computing. J. King Saud Univ. Comput. Inf. Sci. 29, 499–504 (2016)

    Google Scholar 

  30. Zhang, W.; Wong, K.-W.; Yu, H.; Zhu, Z.-L.: An image encryption scheme using reverse 2-dimensional chaotic map and dependent diffusion. Commun. Nonlinear Sci. Numer. Simul. 18(8), 2066–2080 (2013)

    Article  MathSciNet  Google Scholar 

  31. Rawat, N.; Kim, B.; Kumar, R.: Fast digital image encryption based on compressive sensing using structurally random matrices and arnold transform technique. Optik Int. J. Light Electron Opt. 127(4), 2282–2286 (2016)

    Article  Google Scholar 

  32. Zhang, Y.; Xu, B.; Zhou, N.: A novel image compression–encryption hybrid algorithm based on the analysis sparse representation. Opt. Commun. 392, 223–233 (2017)

    Article  Google Scholar 

  33. Sivakumar, T.; Venkatesan, R.: A novel image encryption using calligraphy based scan method and random number. KSII Trans. Internet Inf. Syst. 9(6) (2015)

  34. El-Samie, F.E.A.; Ahmed, H.E.H.; Elashry, I.F.; Shahieen, M.H.; Faragallah, O.S.; El-Rabaie, E.-S.M.; Alshebeili, S.A.: Image Encryption: A Communication Perspective. CRC Press, Boca Raton (2013)

    Book  Google Scholar 

  35. Bakhshandeh, A.; Eslami, Z.: An authenticated image encryption scheme based on chaotic maps and memory cellular automata. Opt. Lasers Eng. 51(6), 665–673 (2013)

    Article  Google Scholar 

  36. Belazi, A.; El-Latif, A.A.A.; Belghith, S.: A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process. 128, 155–170 (2016)

    Article  Google Scholar 

  37. Ghebleh, M.; Kanso, A.; Noura, H.: An image encryption scheme based on irregularly decimated chaotic maps. Signal Process. Image Commun. 29(5), 618–627 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Manjit Kaur & Vijay Kumar

Authors
  1. Manjit Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijay Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manjit Kaur.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, M., Kumar, V. Adaptive Differential Evolution-Based Lorenz Chaotic System for Image Encryption. Arab J Sci Eng 43, 8127–8144 (2018). https://doi.org/10.1007/s13369-018-3355-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-018-3355-3

Keywords

Search

Navigation