Log in

A visually meaningful secure image encryption algorithm based on conservative hyperchaotic system and optimized compressed sensing

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Aiming at the traditional schemes for encrypting and transmitting images can be subject to arbitrary destruction by attackers, making it difficult for algorithms with poor robustness to recover the original image, this paper proposes a new visually image encryption algorithm, which can embed the compressed and encrypted image into a carrier image to achieve visual security, thus avoiding destruction and attacks. Foremost, a new conservative hyperchaotic system without attractors was constructed that can resist reconstruction attacks. Secondly, a two-dimensional (2D) compressed sensing technique is adopted, and the pseudo random sequences of the proposed chaotic system generates a measurement matrix in compressed sensing, and optimizes this matrix to improve the visual quality of image reconstruction. Finally, by combining discrete wavelet transform (DWT) and singular value decomposition (SVD) methods, the encrypted image is embedded into the carrier image to achieve the purpose of image compression, encryption, and hiding. And experimental results and comparative analysis demonstrate that this algorithm has high security, good image reconstruction quality, and strong imperceptibility after image embedding. Under limited bandwidth conditions, the algorithm achieves excellent visual security effects.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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 (United Kingdom)

Instant access to the full article PDF.

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

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Lai, Q., Hu, G., Erkan, U., et al.: High-efficiency medical image encryption method based on 2D Logistic-Gaussian hyperchaotic map. Appl. Math. Comput. 442, 127738 (2023)

    MathSciNet  Google Scholar 

  2. Hua, Z., Zhou, Y.: Exponential chaotic model for generating robust chaos. IEEE Trans Syst, Man, Cybern: Syst. 51(6), 3713–3724 (2021)

    Article  Google Scholar 

  3. Zhou, Z., Xu, X., Yao, Y., et al.: Novel multiple-image encryption algorithm based on a two-dimensional hyperchaotic modular model. Chaos Solitons Fractals 173, 113630 (2023)

    Article  MathSciNet  Google Scholar 

  4. Zhu, S., Deng, X., Zhang, W., et al.: Secure image encryption scheme based on a new robust chaotic map and strong S-box. Math. Comput. Simul 207, 322–346 (2023)

    Article  MathSciNet  Google Scholar 

  5. Wang, T., Wang, M.: Hyperchaotic image encryption algorithm based on bit-level permutation and DNA encoding. Opt. Laser Technol. 132, 106355 (2020)

    Article  Google Scholar 

  6. **ong, L., Yang, F., An, X., et al.: Hyperchaotic system with application to image encryption. Int. J. Bifurc. Chaos. 32(13c), 2250191 (2022)

    Article  MathSciNet  Google Scholar 

  7. Liang, W., Zhang, L., Yang, Z., et al.: Image encryption algorithm based on hyperchaotic system and dynamic DNA encoding. Phys. Scr. 98(11), 115215 (2023)

    Article  Google Scholar 

  8. Yu, F., Shen, H., Yu, Q., et al.: Privacy protection of medical data based on multi-scroll memristive Hopfield neural network. IEEE Trans. Netw. Sci. Eng. 10(2), 845–858 (2023)

    Article  Google Scholar 

  9. Lin, H., Wang, C., Cui, L., et al.: Hyperchaotic memristive ring neural network and application in medical image encryption. Nonlinear Dyn. 110(1), 841–855 (2022)

    Article  Google Scholar 

  10. Qi, G., Hu, J.: Modelling of both energy and volume conservative chaotic systems and their mechanism analyses. Commun. Nonlinear Sci. Numer. Simul. 84, 105171 (2020)

    Article  MathSciNet  Google Scholar 

  11. Liu, X., Tong, X., Wang, Z., et al.: A new n-dimensional conservative chaos based on generalized hamiltonian system and its’ applications in image encryption. Chaos Solitons Fractals 154, 111693 (2022)

    Article  MathSciNet  Google Scholar 

  12. Chai, X., Gan, Z., Chen, Y., et al.: A visually secure image encryption scheme based on compressive sensing. Signal Process. 134, 35–51 (2017)

    Article  Google Scholar 

  13. Hua, Z., Zhang, K., Li, Y., et al.: Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing. Signal Process. 183, 107998 (2021)

    Article  Google Scholar 

  14. Ponuma, R., Amutha, R., Aparna, S., et al.: Visually meaningful image encryption using data hiding and chaotic compressive sensing. Multim. Tools Appl. 78(18), 25707–25729 (2019)

    Article  Google Scholar 

  15. **, P., Fu, J., Mao, Y., et al.: Meaningful encryption: generating visually meaningful encrypted images by compressive sensing and reversible color transformation. IEEE Access. 7, 170168–170184 (2019)

    Article  Google Scholar 

  16. Wen, W., Hong, Y., Fang, Y., et al.: A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Process. 173, 107580 (2020)

    Article  Google Scholar 

  17. Jiang, D., Liu, L., Zhu, L., et al.: Adaptive embedding: a novel meaningful image encryption scheme based on parallel compressive sensing and slant transform. Signal Process. 188, 108220 (2021)

    Article  Google Scholar 

  18. Zhu, L., Jiang, D., Ni, J., et al.: A visually secure image encryption scheme using adaptive-thresholding sparsification compression sensing model and newly- designed memristive chaotic map. Inf. Sci. 607, 1001–1022 (2022)

    Article  Google Scholar 

  19. Gao, J., Chen, M., Xu, C.: Vectorized evidential learning for weakly-supervised temporal action localization. IEEE Trans. Pattern Anal. Mach. Intell. 45(12), 15949–15963 (2023)

    Article  Google Scholar 

  20. Gao, J., Xu, C.: Learning video moment retrieval without a single annotated video. IEEE Trans. Circuits Syst. Video. 32(3), 1646–1657 (2022)

    Article  Google Scholar 

  21. Gao, J., Zhang, T., Xu, C., et al.: Learning to model relationships for zero-shot video classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3476–3491 (2021)

    Article  Google Scholar 

  22. Feng, Y., Gao, J., Yang, S., et al.: Spatial-temporal exclusive capsule Network for open set action recognition. IEEE Trans. Multim. 25, 9464–9478 (2023)

    Article  Google Scholar 

  23. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Article  MathSciNet  Google Scholar 

  24. Chai, X., Zheng, X., Gan, Z., et al.: An image encryption algorithm based on chaotic system and compressive sensing. Signal Process. 148, 124–144 (2018)

    Article  Google Scholar 

  25. Qi, G.: Modelings and mechanism analysis underlying both the 4D Euler equations and Hamiltonian conservative chaotic systems. Nonlinear Dyn. 95(3), 2063–2077 (2019)

    Article  Google Scholar 

  26. Liu, X., Tong, X., Wang, Z., et al.: Construction of controlled multi-scroll conservative chaotic system and its application in color image encryption. Nonlinear Dyn. 110(2), 1897–1934 (2022)

    Article  Google Scholar 

  27. Hua, Z., Zhou, Y.: Design of image cipher using block-based scrambling and image filtering. Inf. Sci. 396, 97–113 (2017)

    Article  Google Scholar 

  28. Liu, X., Tong, X., Wang, Z., et al.: A novel hyperchaotic encryption algorithm for color image utilizing DNA dynamic encoding and self-adapting permutation. Multim. Tools Appl. 81(15), 21779–21810 (2022)

    Article  Google Scholar 

  29. Cang, S., Kang, Z., Wang, Z.: Pseudo-random number generator based on a generalized conservative Sprott-A system. Nonlinear Dyn. 104(1), 827–844 (2021)

    Article  Google Scholar 

  30. Jia, H., Shi, W., Wang, L.: Energy analysis of Sprott-A system and generation of a new Hamiltonian conservative chaotic system with coexisting hidden attractors. Chaos Solitons Fractals 133, 109635 (2020)

    Article  MathSciNet  Google Scholar 

  31. Qi, G., Hu, J., Wang, Z.: Modeling of a Hamiltonian conservative chaotic system and its mechanism routes from periodic to quasiperiodic, chaos and strong chaos. Appl. Math. Model. 78, 350–365 (2020)

    Article  MathSciNet  Google Scholar 

  32. https://sipi.usc.edu/database/database

  33. Chai, X., Wu, H., Gan, Z., et al.: An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt. Lasers Eng. 124, 105837 (2020)

    Article  Google Scholar 

  34. Xu, Q., Sun, K., Cao, C., et al.: A fast image encryption algorithm based on compressive sensing and hyperchaotic map. Opt. Lasers Eng. 121, 203–214 (2019)

    Article  Google Scholar 

  35. Wei, D., Jiang, M.: A fast image encryption algorithm based on parallel compressive and DNA. Optik 238, 166748 (2021)

    Article  Google Scholar 

  36. Zhang, C., Fan, H., Zhang, M., et al.: Plaintext-related image encryption scheme without additional plaintext based on 2DCS. Optik 272, 170312 (2023)

    Article  Google Scholar 

  37. Ren, H., Niu, S., Chen, J., et al.: A visually secure image encryption based on the Fractional Lorenz system and compressive sensing. Fractal Fract. 6(6), 302 (2022)

    Article  Google Scholar 

  38. Su, Y., Wang, X.: A robust visual image encryption scheme based on controlled quantum walks. Phys. A- Stat. Mech. Appl. 587, 126529 (2022)

    Article  Google Scholar 

  39. Wang, X., Liu, C., Jiang, D.: A novel visually meaningful image encryption algorithm based on parallel compressive sensing and adaptive embedding. Expert Syst. Appl. 209, 118426 (2023)

    Article  Google Scholar 

  40. Wang, H., **ao, D., Li, M., et al.: A visually secure image encryption scheme based on parallel compressive sensing. Signal Process. 155, 218–232 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the Shandong Provincial Natural Science Foundation under grant ZR2019MF054 and the National Natural Science Foundation of China under grant 61902091.

Author information

Authors and Affiliations

Authors

Contributions

Tong. X.: Supervision, Methodology, Funding acquisition, Writing–review & editing. Liu. X.: Methodology, Software, Writing–review & editing. Tao. P.: Writing–review & editing. Zhang. M.: Supervision, Funding acquisition. Wang. Z.: Visualization, Supervision.

Corresponding author

Correspondence to **lin Liu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Communicated by J. Gao.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, X., Liu, X., Pan, T. et al. A visually meaningful secure image encryption algorithm based on conservative hyperchaotic system and optimized compressed sensing. Multimedia Systems 30, 168 (2024). https://doi.org/10.1007/s00530-024-01370-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00530-024-01370-4

Keywords

Navigation