Advertisement

Image Encryption Using Cellular Neural Network and Matrix Transformation

  • Gangyi HuEmail author
  • Jian Qu
  • Sumeth Yuenyong
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 807)

Abstract

This paper proposes an image encryption algorithm based on CNN (Cellular Neural Network) chaotic system and matrix transformation. The algorithm uses the initial State of CNN as the encryption key, which generates five-dimensional chaotic sequence. Then the image pixel values were changed by performing XOR operation between the original image pixel values and the modified chaotic sequence. Finally, the pixel positions were changed using a construction matrix, resulting in the cipher image. The experiment results show that this algorithm has good encryption effect, strong key sensitivity and high security.

Keywords

Image encryption Cellular Neural Network Chaotic system 

References

  1. 1.
    Dong, H.S., Lu, P., Ma, X.: Image encryption algorithm based on CNN hyper chaotic system and extend zigzag transformation. Comput. Appl. Softw. 30(5), 133–137 (2013)Google Scholar
  2. 2.
    Sengodan, V., Balamurugan, A.: Efficient signal encryption using chaos-based system. Int. J. Electron. Eng. 2(2), 335–338 (2010)Google Scholar
  3. 3.
    Zhong, H.Q., Li, J.M.: Image encryption scheme based on hyper chaotic sequence. Res. Comput. 30(10), 3110–3113 (2013)Google Scholar
  4. 4.
    Telem, A.N.K., Segnig, C.M., Kenne, G., Fotsin, H.B.: A simple and robust gray image encryption scheme using chaotic logistic map and artificial neural network. Adv. Multimed. 2014(12), 1–13 (2014)Google Scholar
  5. 5.
    Patidar, V., Pareek, N.K., Purohit, G., Sud, K.: Modified substitution diffusion image cipher using chaotic standard and logistic maps. Commun. Nonlinear Sci. Numer. Simul. 15(10), 2755–2765 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Li, G.D., Zhao, G.M., Xu, W.X., Yao, S.Z.: Research on application of image encryption technology based on chaotic of cellular neural network. J. Digit. Inf. Manag. 12(2), 151–158 (2014)Google Scholar
  7. 7.
    Chua, L.O., Yang, L.: Cellular neural networks: theory. IEEE Trans. Circuits Syst. 35(10), 1257–1272 (1988)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ren, X.X., Liao, X.F., Xiong, Y.H.: New image encryption algorithm based on cellular neural network. J. Comput. Appl. 6(11), 1528–1535 (2011)Google Scholar
  9. 9.
    Wang, Y., Wu, C.M., Qiu, S.J.: Block encryption algorithm based on chaotic characteristics of cellular neural networks. Comput. Appl. Softw. 30(11), 191–194 (2013)Google Scholar
  10. 10.
    Li, L.: Study on hyper chaos and hyper chaos synchronization method for cellular neural networks. Dissertation, Harbin Institute of Technology, Harbin, China (2013)Google Scholar
  11. 11.
    Wu, Y.L.: A novel transform matrix used for image scrambling. Electron. Sci. 21(3), 69–72 (2008)Google Scholar
  12. 12.
    Kocarev, L.: Chaos-Based Cryptography, 1st edn. Springer, Berlin (2011)CrossRefGoogle Scholar
  13. 13.
    Dureja, P., Kochhar, B.: Image encryption using Arnold’s cat map and logistic map for secure transmission. Int. J. Comput. Sci. Mob. Comput. 4(6), 194–199 (2015)Google Scholar
  14. 14.
    Singh, V., Dubey, V.: A two level image security based on Arnold transform and chaotic logistic mapping. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(2), 883–887 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Big Data and Intelligence EngineeringSouthwest Forestry UniversityKunmingChina
  2. 2.School of Science and TechnologyShinawatra UniversityPathum ThaniThailand

Personalised recommendations