Skip to main content

Image Encryption with Logistic Chaotic Model Using C-QUATRE Algorithm

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 268))

  • 525 Accesses

Abstract

With the continuous update and progress of the network, a large number of public and private images and other multimedia information are transmitted. When lots of images are transmitted, security is an important aspect. Image encryption is the principal problem to be solved. Among the image encryption methods, the chaotic logistic function is one of the simplest and common methods. In a general encryption system, the initial secret key directly generated by the one value and parameter of the chaotic map is easily cracked by the exhaustive attack. Before generating the initial secret key, this paper applies Competitive QUasi-Affine TRansformation Evolution (C-QUATRE) algorithm to optimize the initial key based on global optimization capability. This novel image chaotic encryption model dramatically improves the effect.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chu, S.C., Chen, Y., Meng, F., Yang, C., Pan, J.S., Meng, Z.: Internal search of the evolution matrix in quasi-affine transformation evolution (QUATRE) algorithm. J. Intell. Fuzzy Syst. 1–12 (2020)

    Google Scholar 

  2. Chu, S.C., Tsai, P.W., Pan, J.S.: Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence, pp. 854–858. Springer (2006)

    Google Scholar 

  3. Fridrich, J.: Image encryption based on chaotic maps. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 2, pp. 1105–1110. IEEE (1997)

    Google Scholar 

  4. Fridrich, J.: Symmetric ciphers based on two-dimensional chaotic maps. Int. J. Bifurc. Chaos 8(06), 1259–1284 (1998)

    Article  MathSciNet  Google Scholar 

  5. Hassan, M.A.S., Abuhaiba, I.S.I.: Image encryption using differential evolution approach in frequency domain. arXiv preprint arXiv:1103.5783 (2011)

  6. Jiang, B.Q., Pan, J.S.: A parallel quasi-affine transformation evolution algorithm for global optimization. J. Netw. Intell. 4(2), 30–46 (2019)

    Google Scholar 

  7. Liu, N., Pan, J.S., Liao, X., Chen, G.: A multi-population quasi-affine transformation evolution algorithm for global optimization. In: International Conference on Genetic and Evolutionary Computing, pp. 19–28. Springer (2018)

    Google Scholar 

  8. Liu, N., Pan, J.S., Wang, J., et al.: An adaptation multi-group quasi-affine transformation evolutionary algorithm for global optimization and its application in node localization in wireless sensor networks. Sensors 19(19), 4112 (2019)

    Article  Google Scholar 

  9. Liu, N., Pan, J.S., et al.: A bi-population quasi-affine transformation evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2019(1), 175 (2019)

    Article  Google Scholar 

  10. Matthews, R.: On the derivation of a “chaotic’’ encryption algorithm. Cryptologia 13(1), 29–42 (1989)

    Article  MathSciNet  Google Scholar 

  11. Meng, Z., Chen, Y., Li, X., Yang, C., Zhong, Y.: Enhancing quasi-affine transformation evolution (QUATRE) with adaptation scheme on numerical optimization. Knowl.-Based Syst. 105908 (2020)

    Google Scholar 

  12. Meng, Z., Pan, J.S.: A competitive quasi-affine transformation evolutionary (C-QUATRE) algorithm for global optimization. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 001,644–001,649. IEEE (2016)

    Google Scholar 

  13. Meng, Z., Pan, J.S.: Quasi-affine transformation evolutionary (QUATRE) algorithm: a parameter-reduced differential evolution algorithm for optimization problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4082–4089. IEEE (2016)

    Google Scholar 

  14. Meng, Z., Pan, J.S.: Quasi-affine transformation evolution with external archive (QUATRE-EAR): an enhanced structure for differential evolution. Knowl.-Based Syst. 155, 35–53 (2018)

    Article  Google Scholar 

  15. Meng, Z., Pan, J.S., Xu, H.: Quasi-affine transformation evolutionary (quatre) algorithm: A cooperative swarm based algorithm for global optimization. Knowl.-Based Syst. 109, 104–121 (2016)

    Article  Google Scholar 

  16. Pan, J.S., Meng, Z., Xu, H., Li, X.: Quasi-affine transformation evolution (QUATRE) algorithm: a new simple and accurate structure for global optimization. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 657–667. Springer (2016)

    Google Scholar 

  17. Pan, J.S., Sun, X.X., Chu, S.C., Abraham, A., Yan, B.: Digital watermarking with improved SMS applied for QR code. Eng. Appl. Artif. Intell. 97, 104,049

    Google Scholar 

  18. Song, P.C., Chu, S.C., Pan, J.S., Yang, H.: Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine. In: 2020 2nd International Conference on Industrial Artificial Intelligence (IAI), pp. 1–5. IEEE (2020)

    Google Scholar 

  19. Song, P.C., Pan, J.S., Chu, S.C.: A parallel compact cuckoo search algorithm for three-dimensional path planning. Appl. Soft Comput. 94, 106,443 (2020)

    Google Scholar 

  20. Sun, X.X., Pan, J.S., Chu, S.C., Hu, P., Tian, A.Q.: A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks. Int. J. Distrib. Sens. Netw. 16(6), 1550147720932,749 (2020)

    Google Scholar 

  21. Tsai, P.W., Pan, J.S., Chen, S.M., Liao, B.Y., Hao, S.P.: Parallel cat swarm optimization. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3328–3333. IEEE (2008)

    Google Scholar 

  22. Tseng, K.K., Zhang, R., Chen, C.M., Hassan, M.M.: Dnetunet: a semi-supervised cnn of medical image segmentation for super-computing ai service. J. Supercomput. 1–22 (2020)

    Google Scholar 

  23. Wang, E.K., Chen, C.M., Hassan, M.M., Almogren, A.: A deep learning based medical image segmentation technique in internet-of-medical-things domain. Futur. Gener. Comput. Syst. 108, 135–144 (2020)

    Article  Google Scholar 

  24. Wu, T.Y., Fan, X., Wang, K.H., Pan, J.S., Chen, C.M.: Security analysis and improvement on an image encryption algorithm using chebyshev generator. J. Internet Technol. 20(1), 13–23 (2019)

    Google Scholar 

  25. Zhang, Y., Zhang, Q., Liao, H., Wu, W., Li, X., Niu, H.: A fast image encryption scheme based on public image and chaos. In: 2017 International Conference on Computing Intelligence and Information System (CIIS), pp. 270–276. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, XX., Pan, JS., Wu, TY., Kong, L., Chu, SC. (2022). Image Encryption with Logistic Chaotic Model Using C-QUATRE Algorithm. In: Zhang, JF., Chen, CM., Chu, SC., Kountchev, R. (eds) Advances in Intelligent Systems and Computing. Smart Innovation, Systems and Technologies, vol 268. Springer, Singapore. https://doi.org/10.1007/978-981-16-8048-9_27

Download citation

Publish with us

Policies and ethics