Skip to main content
SpringerLink
Log in

Computer generated hologram-based image cryptosystem with multiple chaotic systems

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Based on computer generated hologram (CGH) and multiple chaotic systems, a novel image encryption scheme is presented, in which shuffling the positions and changing the values of image pixels are combined to confuse the relationship between the ciphertext and the original image. In the encryption process, the complex distribution is permuted by use of the designed scrambling algorithm which is based on Chen’s chaotic system and logistic maps firstly. Subsequently, the Burch’s coding method is used to fabricate the CGH as the encrypted image. Finally, the pixel values of the encrypted CGH are changed by sine map to withstand statistical analysis attacks. Simulation results demonstrate that the proposed method has high security level and certain robustness against statistical analysis attacks, data loss, noise disturbance and differential attack.

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

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Wang, F., Song, H., Jiang, D., & Wen, H. (2019). Adaboost-based security level classification of mobile intelligent terminals. The Journal of Supercomputing. https://doi.org/10.1007/s11227-019-02954-y.

    Article  Google Scholar 

  2. Liu, Z. J., Xu, L., Lin, C., et al. (2011). Image encryption scheme by using iterative random phase encoding in gyrator transform domains. Optics and Lasers in Engineering, 49(4), 542–546.

    Google Scholar 

  3. Xi, S. X., Wang, X. L., Song, L. P., et al. (2017). Experimental study on optical image encryption with asymmetric double random phase and computer-generated hologram. Optics Express, 25, 8212–8222.

    Google Scholar 

  4. Chen, L., Jiang, D., Song, H., et al. (2018). A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access, 6, 15408–15419.

    Google Scholar 

  5. Zhou, B., Xu, X. T., Liu, J. G., Xu, X. K., & Wang, N. X. (2019). Information interaction model for the mobile communication networks. Physica A: Statistical Mechanics and its Applications, 525, 1170–1176.

    Google Scholar 

  6. Huo, L., & Jiang, D. (2019). Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommunication System, 23(4), 1–11.

    Google Scholar 

  7. Fekher, K., Abbas, B., Abderrahim, B., Priyanka, R., & Mohamed, A. (2019). A survey of localization systems in internet of things. Mobile Networks and Applications, 24(3), 761–785.

    Google Scholar 

  8. Huo, L., Jiang, D., Zhu, X., et al. (2019). An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. International Journal of Communication Systems. https://doi.org/10.1002/dac.4092.

    Article  Google Scholar 

  9. Mouratidis, H., Shei, S., & Delaney, A. (2019). A security requirements modelling language for cloud computing environments. Software & Systems Modeling. https://doi.org/10.1007/s10270-019-00747-8.

    Article  Google Scholar 

  10. Sun, M., Jiang, D., Song, H., et al. (2017). Statistical resolution limit analysis of two closely-spaced signal sources using Rao test. IEEE Access, 5, 22013–22022.

    Google Scholar 

  11. Jiang, D., Zhang, P., Lv, Z., et al. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3(6), 1437–1447.

    Google Scholar 

  12. Kong, L., & Ma, B. (2019). Intelligent manufacturing model of construction industry based on Internet of Things technology. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-04369-8.

    Article  Google Scholar 

  13. Jiang, D., Li, W., & Lv, H. (2017). An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing, 220, 160–169.

    Google Scholar 

  14. Huo, L., Jiang, D., & Lv, Z. (2018). Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks. Computers & Electrical Engineering, 66(2), 316–331.

    Google Scholar 

  15. Krichen, M. (2019). Improving formal verification and testing techniques for internet of things and smart Cities. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01369-6.

    Article  Google Scholar 

  16. Jiang, D., Huo, L., & Song, H. (2018). Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Transactions on Network Science and Engineering, 1(1), 1–12.

    MathSciNet  Google Scholar 

  17. Yu, G., & Liu, J. (2019). A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion. Applied Intelligence, 49(4), 1421–1436.

    Google Scholar 

  18. Jiang, D., Wang, Y., Lv, Z., et al. (2019). Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2930226.

    Article  Google Scholar 

  19. Wang, F., Jiang, D., & Qi, S. (2019). An adaptive routing algorithm for integrated information networks. China Communications, 7(1), 196–207.

    Google Scholar 

  20. Riisa, S., & Gadouleaub, M. (2019). Max-flow min-cut theorems on dispersion and entropy measures for communication networks. Information and Computation, 267, 49–73.

    MathSciNet  Google Scholar 

  21. Jiang, D., Huo, L., Lv, Z., et al. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intelligent Transportation Systems, 19(10), 3305–3319.

    Google Scholar 

  22. Ranjith Kumar, A., & Sivagami, A. (2019). Security aware multipath routing protocol for WMSNs for minimizing effect of compromising attacks. Journal of Network and Systems Management, 27(3), 573–599.

    Google Scholar 

  23. Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE, 13(5), 1–23.

    Google Scholar 

  24. Zhu, J., Song, Y., Jiang, D., et al. (2018). A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of things. IEEE Internet of Things Journal, 5(4), 2375–2385.

    Google Scholar 

  25. Tian, R., & Zhu, H. (2019). Complex application identification and private network mining algorithm based on traffic-aware model in large-scale networks. Peer-to-Peer Networking and Applications. https://doi.org/10.1007/s12083-019-00803-6.

    Article  Google Scholar 

  26. Jiang, D., Wang, W., Shi, L., et al. (2018). A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering, 5(3), 1–12.

    Google Scholar 

  27. Refregier, P., & Javidi, B. (1995). Optical image encryption based on input plane and Fourier plane random encoding. Optics Letters, 20, 767–769.

    Google Scholar 

  28. Li, X. W., Kim, S. T., & Wang, Q. H. (2017). Copyright protection for elemental image array by hypercomplex Fourier transform and an adaptive texturized holographic algorithm. Optics Express, 25, 17076–17091.

    Google Scholar 

  29. Nischal, N. K., Joseph, J., & Singh, K. (2004). Securing information using fractional Fourier transform in digital holography. Optics Communications, 235, 253–259.

    Google Scholar 

  30. Lee, I. H., & Cho, M. J. (2014). Double random phase encryption using orthogonal encoding for multiple-image transmission. Journal of the Optical Society of Korea, 18, 201–206.

    Google Scholar 

  31. Li, X. X., & Zhao, D. M. (2010). Optical color image encryption with redefined fractional Hartley transform. Optik, 121(7), 673–677.

    Google Scholar 

  32. Alfalou, A., & Brosseau, C. (2013). Implementing compression and encryption of phase shifting digital holograms for three-dimensional object reconstruction. Optics Communications, 307, 67–72.

    Google Scholar 

  33. Guo, Y., Huang, Q., Du, J., & Zhan, Y. (2018). Decomposition storage of information based on computer-generated hologram interference and its application in optical image encryption. Applied Optics, 40, 2860–2863.

    Google Scholar 

  34. Xi, S. X., Wang, X. L., Sun, X., et al. (2013). Three random phase encryption technology in the Fresnel diffraction system based on computer-generated hologram. Optical Engineering, 53(1), 011004.

    Google Scholar 

  35. Pan, W., Wun, W. W., & Zhang, X. L. (2009). Encryption algorithm of virtual optical based on computer-generated hologram and double random phase. Chinese Journal of Lasers, 36(s2), 312–317.

    Google Scholar 

  36. Wang, Y. Y., Wang, Y. R., Wang, Y., et al. (2007). Optical image encryption based on binary Fourier transform computer-generated hologram and pixel scrambling technology. Optics and Lasers in Engineering, 45(7), 761–765.

    Google Scholar 

  37. Liu, J., Jin, H. Z., Ma, L. H., Li, Y., & Jin, W. M. (2013). Optical color image encryption based on computer generated hologram and chaotic theory. Optics Communications, 307, 76–79.

    Google Scholar 

  38. Wu, J. H., Luo, X. Z., Zhou, N. R., et al. (2013). Four-image encryption method based on spectrum truncation, chaos and the MODFrFT. Optics & Laser Technology, 45, 571–577.

    Google Scholar 

  39. Tricoles, G. (1987). Computer generated holograms: An historical review. Applied Optics, 26, 4351–4357.

    Google Scholar 

  40. Guan, Z. H., Huang, F. J., Guan, W. J., et al. (2005). Chaos-based image encryption algorithm. Physics Letters A, 346(1), 153–157.

    MATH  Google Scholar 

  41. Belazi, A., & Ellatif, A. A. (2017). A simple yet efficient S-box method based on chaotic sine map. Optik, 130, 1438–1444.

    Google Scholar 

  42. He, M. Z., Tan, Q. F., Cao, L. C., He, Q. S., & Jin, G. F. (2009). Security enhanced optical encryption system by random phase key and permutation key. Optics Express, 17, 22462–22473.

    Google Scholar 

  43. Smila, M., & Sankar, S. (2014). Novel algorithms for finding an optimal scanning path for JPEG image compression. IJETCSE, 8, 230–236.

    Google Scholar 

  44. Shen, J. J., & Ren, J. M. (2010). A robust associative watermarking technique based on vector quantization. Digital Signal Process, 20, 1408–1423.

    Google Scholar 

  45. Zhang, Y., & Xiao, D. (2013). Double optical image encryption using discrete Chirikov standard map and chaos-based fractional random transform. Optics and Lasers in Engineering, 51, 472–480.

    Google Scholar 

  46. Akhshani, A., Behnia, S., Akhavan, A., et al. (2010). A novel scheme for image encryption based on 2D piecewise chaotic maps. Optics Communications, 283(17), 3259–3266.

    MATH  Google Scholar 

  47. Gonzalez, R. C., & Woods, R. E. (2007). Digital Image Processing (3rd ed.). New Jersey: Prentice Hall.

    Google Scholar 

  48. Guo, C. L., Liu, S., & Sheridan, J. T. (2014). Optical double image encryption employing a pseudo image technique in the Fourier domain. Optics Communications, 321, 61–72.

    Google Scholar 

  49. Ping, P., Xu, F., Mao, Y. C., & Zj, Wang. (2018). Designing permutation–substitution image encryption networks with Henon map. Neurocomputing, 283, 53–63.

    Google Scholar 

  50. Zhang, J., Fang, D. X., & Ren, H. E. (2014). Image encryption algorithm based on DNA encoding and chaotic maps. Mathematical Problems in Engineering, 917147, 1–10.

    Google Scholar 

Download references

Acknowledgements

This work is partly supported by the Natural Science Foundation of Guangdong Province (No.2018A0303070009 and No. 2014A030310038), the Educational Commission of Guangdong Province (No. 2018KTSCX143 and No. 2015KTSCX089).

Author information

Authors and Affiliations

  1. School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China

    Chuying Yu

  2. School of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China

    Xiaowei Li

  3. College of Mathematics and Statistics, Hanshan Normal University, Chaozhou, 521041, China

    Shaoyuan Xu & Jianzhong Li

Authors
  1. Chuying Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaowei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaoyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianzhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianzhong Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, C., Li, X., Xu, S. et al. Computer generated hologram-based image cryptosystem with multiple chaotic systems. Wireless Netw 27, 3507–3521 (2021). https://doi.org/10.1007/s11276-019-02223-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-02223-z

Keywords

Search

Navigation