Effective Image Encryption Technique Through 2D Cellular Automata

  • Rupali BhardwajEmail author
  • Vaishalli Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)


Basic idea of encryption is to encrypt the pixels of an image in such a manner that the image becomes chaotic and indistinguishable. In this paper, we have done exploratory study on image encryption techniques through 2D cellular automata (single layer and double layer) which create chaos on pixels of considered image. A comparative analysis on effectiveness of scrambling technique is provided by scrambling degree measurement parameters, i.e., gray difference degree (GDD) and correlation coefficient. Experimental results showed that the 2D cellular automata (single layer)-based encryption gives better result than 2D cellular automata (double layer).


Encryption Cellular automata Game of Life Gray difference degree Correlation coefficient 


  1. 1.
    Abdel Latif Abu Dalhoum, Ibrahim Aldamari “Digital Image Scrambling Using 2D Cellular Automata”, 14th IEEE International Symposium on Multimedia, 2012. Google Scholar
  2. 2.
    Congli Wang, Zhibin Chen, Ting Li, “Blind Evaluation of Image Scrambling Degree based on the Correlation of Adjacent Pixels”, Telkomnika, Indonesian Journal of Electrical Engineering, Vol. 11, No. 11, pp. 6556–6562, November 2013.Google Scholar
  3. 3.
    Fasel Qadir, M. A. Peer, K. A. Khan, “Digital Image Scrambling Based on Two Dimensional Cellular Automata”, International Journal of Computer Network and Information Security, Vol. 2, pp 36–41, 2013.Google Scholar
  4. 4.
    Gabriel Peterson, “Arnold’s Cat Map”, Newyork, 1997.Google Scholar
  5. 5.
    Lingling Wu, Jianwei Zhang, Weitao Deng, Dongyan He, “Arnold Transformation Algorithm and Anti-Arnold Transformation Algorithm”, 1st International Conference on Information Science and Engineering (ICISE2009), IEEE.Google Scholar
  6. 6.
    Mao-Yu Huang, Yueh-Min Huang, Ming-Shi Wang, “Image Encryption Algorithm Based on Chaotic Maps”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010, IEEE.Google Scholar
  7. 7.
    Min Li, Ting Liang, Yu-jie He, “Arnold Transform Based Image Scrambling Method”, 3rd International Conference on Multimedia Technology (ICMT 2013).Google Scholar
  8. 8.
    Pawan N. K., Narnaware, M., “Practical Approaches for Image Encryption/Scrambling Using 3D Arnolds Cat Map”, CNC 2012, LNICST 108, pp 398–404, 2012.Google Scholar
  9. 9.
    Ruisong Ye, “A Novel Image Scrambling and Watermarking Scheme Based on Orbits of Arnold Transform”, Pacific-Asia Conference on Circuits, Communications and System, 2009, IEEE.Google Scholar
  10. 10.
  11. 11.
    V. I. Arnold, A. Avez, “Ergodic Problems in Classical Mechanics”, New York: Benjamin, 1968.Google Scholar
  12. 12.
    Ruisong Ye, Huiliang Li, “A Novel Image Scrambling and Watermarking Scheme Based on Cellular Automata”, International Symposium on Electronic Commerce and Security, 2008, IEEE.Google Scholar
  13. 13.
    Tan Yongjie, Zhou Wengang, “Image Scrambling Degree Evaluation Algorithm based on Grey Relation Analysis”, International Conference on Computational and Information Sciences, 2010, IEEE.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringThapar UniversityPatialaIndia

Personalised recommendations