Images encryption by the use of evolutionary algorithms | Analog Integrated Circuits and Signal Processing
Skip to main content

Images encryption by the use of evolutionary algorithms

  • Published:
Analog Integrated Circuits and Signal Processing Aims and scope Submit manuscript

Abstract

Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission are very problematical. So, encryption of the transmitted data is one of the most promising solutions. In our work, we focus on the security of image data, which are considered as specific data because of their big size and their information which are of two-dimensional nature and also redundant. These data characteristics make the developed algorithms in the literature unavailable in their classical forms, because of the speed and the possible risk of information loss. In this paper, we develop an original “images encryption” algorithm based on evolutionary algorithms. The appropriateness of the proposed scheme is demonstrated by the sensitivity to images, the key and the resistibility to various advanced attacks.

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

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Souici, I. Seridi, H., & Aissaoui, Z. (2007). Nouvel algorithme de chiffrement évolutionnaire ACEO. JIG’07. 3èmes Journées internationales sur l’Informatique Graphique. Constantine. Algérie.

  2. Souici, I., Aissaoui, Z., & Seridi, H. (2008). Conception et Evaluation d’un Nouvel Algorithme Crypto-évolutionnaire (SSA). COSI’08. Colloque sur l’Optimisation et les Systèmes d’Information. Tizi-Ouzou. Algérie.

  3. Katzenbeisser, S., & Petitcolas, F. A. P. (2000). Information hiding techniques for steganography and digital watermarking. London: Artech House.

    Google Scholar 

  4. Kahn, D. (1996). The Codebreakers—The story of secret writing. New York: Scribner.

    Google Scholar 

  5. Kobayashi, M. (1990). Digital watermarking: Historical roots. Technical report, IBM Research, Tokyo Research Laboratory. Japan.

  6. Darwin, C. (1859). On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. London: John Murray.

    Google Scholar 

  7. Fogel, L., Owens, A., & Walsh, M. (1966). Artificial intelligence through simulated evolution. Chichester, UK: Wiley. In (Jour, 2003).

  8. Rechenberg, I. (1973). Evolutions Strategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Frommann-Holzboog. In [Jour, 2003].

  9. Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  10. Koza, J. (1992). Genetic programming. Cambridge, MA: MIT Press.

    MATH  Google Scholar 

  11. Prabhu, D., Buckles, B. P., & Petry, F. E. (2000). Genetic algorithms for scene interpretation from prototypical semantic description. International Journal of Intelligent Systems, 15(10), 901–918.

    Article  MATH  Google Scholar 

  12. Grenfenslette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transaction on Systems, Man, and Cybernetics, 16(1), 122–128.

    Article  Google Scholar 

  13. Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In Proceedings of the third international conference on genetic algorithms (ICGA’89), George Mason University, Fairfax, Virginia, USA, June 1989 (pp. 61–69).

  14. Bäck, T. (1997). Mutation parameters. Handbook of evolutionary computation. 97/1, E1.2, IOP Publishing Ltd.

  15. Hutter, F., Hamadi, Y., Hoos, H. H., & Leyton-Brown, K. (2006). Performance prediction and automated tuning of randomized and parametric algorithms. In CP 2006, number 4204 in LNCS (pp. 213–228). Springer Verlag.

  16. Eiben, A. E., Michalewicz, Z., Schoenauer, M., & Smith, J. E. (2007). Parameter control in evolutionary algorithms. In F. G. Lobo, C. F. Lima, & Z. Michalewicz (Eds.), Parameter setting in evolutionary algorithms, studies in computational intelligence (pp. 19–46). Springer.

  17. Bibai, J., Savéant, P., Schoenauer, M., & Vidal, V. (2010). On the generality of parameter tuning in evolutionary planning. In ACM Genetic and Evolutionary Computation Conference (GECCO-2010) (pp. 241–248). Portland, Oregon: United States.

  18. Djeddi, M., Ouahabi, A., Batatia, H., Basarab, A., & Kouamé, D. (2010). Discrete wavelet for multifractal texture classification: Application to medical ultrasound imaging. In IEEE international conference on image processing, Hong Kong.

  19. Ouahabi, A. & Ait Aouit, D. (2008). Wavelets and fractals for signal and image analysis. In P. Siarry (Ed.), Optimization in signal and image processing (pp. 45–77). London: ISTE; Hoboken: Wiley.

  20. Kerckhoffs, A. (1983). La cryptographie militaire. Journal des sciences militaires, IX, 5–38.

  21. Shannon, C. (1949). Communication theory of secrecy systems. Bell Systems Technical Journal, 28, 656–715.

    MathSciNet  MATH  Google Scholar 

  22. Stinson, D. (1996). Cryptographie, théorie et pratique. France: International Thomson Publishing.

    Google Scholar 

  23. Biham, E., & Shamir, A. (1991). Differential cryptanalysis of DES-like cryptosystems. Journal of Cryptology, 4(1), 3–72.

    Article  MathSciNet  MATH  Google Scholar 

  24. Matsui, M. (1994). Linear cryptanalysis method for DES cipher. Advances in cryptology, EUROCRYPT’93. Lecture Notes in Computer Science (Vol. 765). Berlin: Springer-Verlag.

  25. Ganteaut, A., & Lévy, F. (2001). La cryptologie moderne. L’Armement, 73, 76–83.

    Google Scholar 

  26. Leprévost, F. (2000). Les standards cryptographiques du XXIe siècle : AES et IEEE-P1363. Gazette des Mathématiciens - n°85.

  27. Omary, F., Tragha, A., Bellaachia, A., Lbekouri, A., & Mouloudi, et. A. (2007). Design and evaluation of two symmetrical evolutionist-based ciphering algorithms. IJCSNS International Journal of Computer Science and Network Security, 7(2), 181–190.

    Google Scholar 

  28. Amrani, Z., Chitroub, S., & Boukhari, A. (2007). Cryptage d’Images par Chiffrement de Vigenère Basé sur le Mixage des Cartes Chaotiques. In 4th International conference on computer integrated manufacturing CIP’2007. Algérie.

  29. Ghislaine Labouret, introduction à la cryptologie, 1998. http://www.labouret.net/crypto/.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. Souici.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Souici, I., Seridi, H. & Akdag, H. Images encryption by the use of evolutionary algorithms. Analog Integr Circ Sig Process 69, 49–58 (2011). https://doi.org/10.1007/s10470-011-9627-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10470-011-9627-4

Keywords