Skip to main content
SpringerLink
Log in

A lossless image compression and encryption algorithm combining JPEG-LS, neural network and hyperchaotic system

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, a lossless image compression and encryption algorithm combining JPEG-LS, neural networks and hyperchaotic mapping is proposed to protect the privacy of digital images and reduce data storage space. Firstly, we design a new 2-Dimensional Logistic-Like Hyperchaotic Map (2DLLHM), which has more complex dynamics than some existing known chaotic systems, and can be used to build a good pseudorandom sequence generator. Secondly, to compress images efficiently, we design a new pixel predictor by combining the MED (Median Edge Detector) of JPEG-LS with MLP (Multilayer Perceptron). This predictor is called MMP. The MMP can effectively improve the prediction effect of edge texture area. On this basis, a threshold segmentation method is proposed. The method combined with MMP, run-length coding and Huffman coding can further improve the image compression ratio. Finally, to avoid some of the existing weak encryption designs, we construct a multi-round nonlinear diffusion structure with more excellent diffusion performance. Experiments show that the algorithm achieves a good compression ratio and can resist brute force attacks, statistical attacks, chosen-plaintext attacks and chosen-ciphertext attacks.

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
Fig. 20

Similar content being viewed by others

References

  1. Ma, B., Shi, Y.Q.: A reversible data hiding scheme based on code division multiplexing. IEEE Trans. Inf. Forensics Secur. 11(9), 1914–1927 (2016)

    Google Scholar 

  2. Li, Q., Wang, X., Ma, B., Wang, X., Wang, C., Gao, S., Shi, Y.: Concealed attack for robust watermarking based on generative model and perceptual loss. IEEE Trans. Circ. Syst. Video Technol. 32, 5695 (2021)

    Google Scholar 

  3. Wang, X., Wang, X., Ma, B., Li, Q., Shi, Y.Q.: High precision error prediction algorithm based on ridge regression predictor for reversible data hiding. IEEE Signal Process. Lett. 28, 1125–1129 (2021)

    Google Scholar 

  4. Zhang, G., Liu, Q.: A novel image encryption method based on total shuffling scheme. Opt. Commun. 284(12), 2775–2780 (2011)

    Google Scholar 

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

    Google Scholar 

  6. Zhang, H., Wang, X.Q., Sun, Y.J., Wang, X.Y.: A novel method for lossless image compression and encryption based on lwt, spiht and cellular automata. Signal Process. Image Commun. 84, 115829 (2020)

    Google Scholar 

  7. Kaur, M., Kumar, V.: A comprehensive review on image encryption techniques. Arch. Comput. Method Eng. 27(1), 15–43 (2020)

    MathSciNet  Google Scholar 

  8. 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)

  9. Chen, G., Mao, Y., Chui, C.K.: A symmetric image encryption scheme based on 3d chaotic cat maps. Chaos Solitons Fractals 21(3), 749–761 (2004)

    MathSciNet  MATH  Google Scholar 

  10. Pareek, N.K., Patidar, V., Sud, K.K.: Image encryption using chaotic logistic map. Image Vis. Comput. 24(9), 926–934 (2006)

    Google Scholar 

  11. Wang, Y., Wong, K.W., Liao, X., Xiang, T., Chen, G.: A chaos-based image encryption algorithm with variable control parameters. Chaos, Solitons Fractals 41(4), 1773–1783 (2009)

    MATH  Google Scholar 

  12. Wang, X., Teng, L., Qin, X.: A novel colour image encryption algorithm based on chaos. Signal Process. 92(4), 1101–1108 (2012)

    MathSciNet  Google Scholar 

  13. Zhou, Y., Bao, L., Chen, C.P.: A new 1d chaotic system for image encryption. Signal Process. 97, 172–182 (2014)

    Google Scholar 

  14. Chai, X., Chen, Y., Broyde, L.: A novel chaos-based image encryption algorithm using dna sequence operations. Opt. Lasers Eng. 88, 197–213 (2017)

    Google Scholar 

  15. Xu, C., Sun, J., Wang, C.: An image encryption algorithm based on random walk and hyperchaotic systems. Int. J. Bifurc. Chaos 30(04), 2050060 (2020)

    MathSciNet  Google Scholar 

  16. Zhang, Y.: The fast image encryption algorithm based on lifting scheme and chaos. Inf. Sci. 520, 177–194 (2020)

    MathSciNet  MATH  Google Scholar 

  17. Hua, Z., Li, J., Chen, Y., Yi, S.: Design and application of an s-box using complete latin square. Nonlinear Dyn. 104(1), 807–825 (2021)

    Google Scholar 

  18. Gao, S., Wu, R., Wang, X., Wang, J., Li, Q., Wang, C., Tang, X.: A 3d model encryption scheme based on a cascaded chaotic system. Signal Process. 202, 108745 (2023)

    Google Scholar 

  19. Sha, Y., Bo, S., Yang, C., Mou, J., Jahanshahi, H.: A chaotic image encryption scheme based on genetic central dogma and kmp method. Int. J. Bifurc. Chaos 32(12), 2250186 (2022)

    MathSciNet  MATH  Google Scholar 

  20. Wang, L., Cao, Y., Jahanshahi, H., Wang, Z., Mou, J.: Color image encryption algorithm based on double layer josephus scramble and laser chaotic system. Optik 275, 170590 (2023)

    Google Scholar 

  21. Ren, L., Mou, J., Banerjee, S., Zhang, Y.: A hyperchaotic map with a new discrete memristor model: design, dynamical analysis, implementation and application. Chaos Solitons Fractals 167, 113024 (2023)

    MathSciNet  Google Scholar 

  22. Li, S., Zheng, X.: Cryptanalysis of a chaotic image encryption method. In: 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No. 02CH37353), vol. 2, pp. II–II. IEEE (2002)

  23. Li, S., Li, C., Chen, G., Bourbakis, N.G., Lo, K.T.: A general quantitative cryptanalysis of permutation-only multimedia ciphers against plaintext attacks. Signal Process. Image Commun. 23(3), 212–223 (2008)

    Google Scholar 

  24. Solak, E., Cokal, C., Yildiz, O.T., Biyikoğlu, T.: Cryptanalysis of fridrich’s chaotic image encryption. Int. J. Bifurc. Chaos 20(05), 1405–1413 (2010)

    MathSciNet  MATH  Google Scholar 

  25. Li, C., Xie, T., Liu, Q., Cheng, G.: Cryptanalyzing image encryption using chaotic logistic map. Nonlinear Dyn. 78(2), 1545–1551 (2014)

    Google Scholar 

  26. Xie, E.Y., Li, C., Yu, S., Lü, J.: On the cryptanalysis of fridrich’s chaotic image encryption scheme. Signal Process. 132, 150–154 (2017)

    Google Scholar 

  27. Li, C., Lin, D., Lü, J., Hao, F.: Cryptanalyzing an image encryption algorithm based on autoblocking and electrocardiography. IEEE Multimed. 25(4), 46–56 (2018)

    Google Scholar 

  28. Chen, J., Chen, L., Zhou, Y.: Cryptanalysis of image ciphers with permutation-substitution network and chaos. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2494–2508 (2020)

    Google Scholar 

  29. Alvarez, G., Li, S.: Some basic cryptographic requirements for chaos-based cryptosystems. Int. J. Bifurc. chaos 16(08), 2129–2151 (2006)

    MathSciNet  MATH  Google Scholar 

  30. Özkaynak, F.: Brief review on application of nonlinear dynamics in image encryption. Nonlinear Dyn. 92(2), 305–313 (2018)

    Google Scholar 

  31. Zhou, N., Zhang, A., Zheng, F., Gong, L.: Novel image compression-encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Opt. Laser Technol. 62, 152–160 (2014)

    Google Scholar 

  32. Zhu, S., Zhu, C., Wang, W.: A novel image compression-encryption scheme based on chaos and compression sensing. IEEE Access 6, 67095–67107 (2018)

    Google Scholar 

  33. Song, Y., Zhu, Z., Zhang, W., Guo, L., Yang, X., Yu, H.: Joint image compression-encryption scheme using entropy coding and compressive sensing. Nonlinear Dyn. 95(3), 2235–2261 (2019)

    MATH  Google Scholar 

  34. Yang, F., Mou, J., Cao, Y., Chu, R.: An image encryption algorithm based on bp neural network and hyperchaotic system. China Commun. 17(5), 21–28 (2020)

    Google Scholar 

  35. Mou, J., Yang, F., Chu, R., Cao, Y.: Image compression and encryption algorithm based on hyper-chaotic map. Mobile Netw. Appl. 1–13 (2019)

  36. Weinberger, M.J., Seroussi, G., Sapiro, G.: The loco-i lossless image compression algorithm: Principles and standardization into jpeg-ls. IEEE Trans. Image Process. 9(8), 1309–1324 (2000)

    Google Scholar 

  37. Bellard, F.: Bpg image format. https://bellard.org/bpg/

  38. Taubman, D.S., Marcellin, M.W.: Jpeg 2000: Standard for interactive imaging. Proc. IEEE 90(8), 1336–1357 (2002)

    Google Scholar 

  39. Alakuijala, J., Van Asseldonk, R., Boukortt, S., Bruse, M., Comsa, I.M., Firsching, M., Fischbacher, T., Kliuchnikov, E., Gomez, S., Obryk, R., et al.: Jpeg xl next-generation image compression architecture and coding tools. In: Applications of Digital Image Processing XLII, vol. 11137, p. 111370K. International Society for Optics and Photonics (2019)

  40. Boutell, Thomas: Png (portable network graphics) specification version 1.0. https://www.hjp.at/doc/rfc/rfc2083.html

  41. Google: Webp: Compression techniques. https://developers.google.com/speed/webp

  42. Sneyers, J., Wuille, P.: Flif: Free lossless image format based on maniac compression. In: 2016 IEEE international conference on image processing (ICIP), pp. 66–70. IEEE (2016)

  43. Wu, X., Memon, N.: Context-based, adaptive, lossless image coding. IEEE Trans. Commun. 45(4), 437–444 (1997)

    Google Scholar 

  44. Cheng, H., Li, X.: Partial encryption of compressed images and videos. IEEE Trans. Signal Process. 48(8), 2439–2451 (2000)

    Google Scholar 

  45. Maniccam, S., Bourbakis, N.G.: Lossless image compression and encryption using scan. Pattern Recogn. 34(6), 1229–1245 (2001)

    MATH  Google Scholar 

  46. Kumar, A.A., Makur, A.: Distributed source coding based encryption and lossless compression of gray scale and color images. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing, pp. 760–764. IEEE (2008)

  47. Zhu, H., Zhao, C., Zhang, X.: A novel image encryption-compression scheme using hyper-chaos and chinese remainder theorem. Signal Process. Image Commun. 28(6), 670–680 (2013)

    Google Scholar 

  48. Liu, W., Zeng, W., Dong, L., Yao, Q.: Efficient compression of encrypted grayscale images. IEEE Trans. Image Process. 19(4), 1097–1102 (2009)

    MathSciNet  MATH  Google Scholar 

  49. Masmoudi, A., Puech, W.: Lossless chaos-based crypto-compression scheme for image protection. IET Image Proc. 8(12), 671–686 (2014)

    Google Scholar 

  50. Tong, X.J., Chen, P., Zhang, M.: A joint image lossless compression and encryption method based on chaotic map. Multimed. Tools Appl. 76(12), 13995–14020 (2017)

    Google Scholar 

  51. Kurihara, K., Imaizumi, S., Shiota, S., Kiya, H.: An encryption-then-compression system for lossless image compression standards. IEICE Trans. Inf. Syst. 100(1), 52–56 (2017)

    Google Scholar 

  52. Zhang, M., Tong, X., Wang, Z., Chen, P.: Joint lossless image compression and encryption scheme based on calic and hyperchaotic system. Entropy 23(8), 1096 (2021)

    MathSciNet  Google Scholar 

  53. Ahmad, I., Shin, S.: A novel hybrid image encryption-compression scheme by combining chaos theory and number theory. Signal Process. Image Commun. 98, 116418 (2021)

    Google Scholar 

  54. Rhee, H., Jang, Y.I., Kim, S., Cho, N.I.: Lossless image compression by joint prediction of pixel and context using duplex neural networks. IEEE Access 9, 86632–86645 (2021)

    Google Scholar 

  55. Gonzalez, R.C.: Digital image processing. Pearson education india (2009)

  56. Weinberger, M.J., Seroussi, G., Sapiro, G.: Loco-i: A low complexity, context-based, lossless image compression algorithm. In: Proceedings of Data Compression Conference-DCC’96, pp. 140–149. IEEE (1996)

  57. Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)

    Google Scholar 

  58. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)

    MATH  Google Scholar 

  59. Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)

    MATH  Google Scholar 

  60. Strogatz, S.H.: Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC press (2018)

  61. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Google Scholar 

  62. Richman, J.S., Lake, D.E., Moorman, J.R.: Sample entropy. In: Methods in enzymology, vol. 384, pp. 172–184. Elsevier (2004)

  63. Wang, M., An, M., Zhang, X., Iu, H.H.C.: Two-variable boosting bifurcation in a hyperchaotic map and its hardware implementation. Nonlinear Dyn. 1–19 (2022)

  64. Bao, H., Hua, Z., Li, H., Chen, M., Bao, B.: Discrete memristor hyperchaotic maps. IEEE Trans. Circuits Syst. I Regul. Pap. 68(11), 4534–4544 (2021)

    Google Scholar 

  65. Bao, B., Li, H., Zhu, L., Zhang, X., Chen, M.: Initial-switched boosting bifurcations in 2d hyperchaotic map. Chaos Interdiscip. J. Nonlinear Sci. 30(3), 033107 (2020)

    MathSciNet  MATH  Google Scholar 

  66. Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E.: A statistical test suite for random and pseudorandom number generators for cryptographic applications. Tech. rep, Booz-allen and hamilton inc mclean va (2001)

  67. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  68. Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 126–135 (2017)

  69. Rhee, H., Jang, Y.I., Kim, S., Cho, N.I.: Channel-wise progressive learning for lossless image compression. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1113–1117. IEEE (2020)

  70. Preishuber, M., Hütter, T., Katzenbeisser, S., Uhl, A.: Depreciating motivation and empirical security analysis of chaos-based image and video encryption. IEEE Trans. Inf. Forensics Secur. 13(9), 2137–2150 (2018)

    Google Scholar 

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

    Google Scholar 

  72. Hua, Z., Xu, B., Jin, F., Huang, H.: Image encryption using josephus problem and filtering diffusion. IEEE Access 7, 8660–8674 (2019)

    Google Scholar 

  73. Hua, Z., Zhou, Y., Huang, H.: Cosine-transform-based chaotic system for image encryption. Inf. Sci. 480, 403–419 (2019)

    Google Scholar 

  74. Ali, T.S., Ali, R.: A new chaos based color image encryption algorithm using permutation substitution and boolean operation. Multimed. Tools Appl. 79(27), 19853–19873 (2020)

    Google Scholar 

  75. Sneha, P., Sankar, S., Kumar, A.S.: A chaotic colour image encryption scheme combining walsh-hadamard transform and arnold-tent maps. J. Ambient. Intell. Humaniz. Comput. 11(3), 1289–1308 (2020)

    Google Scholar 

  76. Talhaoui, M.Z., Wang, X., Midoun, M.A.: A new one-dimensional cosine polynomial chaotic map and its use in image encryption. Vis. Comput. 37(3), 541–551 (2021)

    Google Scholar 

  77. Wu, Y., Zhou, Y., Saveriades, G., Agaian, S., Noonan, J.P., Natarajan, P.: Local shannon entropy measure with statistical tests for image randomness. Inf. Sci. 222, 323–342 (2013)

    MathSciNet  MATH  Google Scholar 

  78. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)

  79. Hua, Z., Zhu, Z., Yi, S., Zhang, Z., Huang, H.: Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Inf. Sci. 546, 1063–1083 (2021)

    MathSciNet  Google Scholar 

  80. Wu, Y., Noonan, J.P., Agaian, S., et al.: Npcr and uaci randomness tests for image encryption Cyber. J. Multidiscip. J. Sci. Technol. J. Selected Areas Telecommun. (JSAT) 1(2), 31–38 (2011)

  81. Wang, H., Xiao, D., Chen, X., Huang, H.: Cryptanalysis and enhancements of image encryption using combination of the 1d chaotic map. Signal Process. 144, 444–452 (2018)

    Google Scholar 

  82. Gan, Z., Bi, J., Ding, W., Chai, X.: Exploiting 2d compressed sensing and information entropy for secure color image compression and encryption. Neural Comput. Appl. 33(19), 12845–12867 (2021)

  83. Gan, Z., Chai, X., Zhang, J., Zhang, Y., Chen, Y.: An effective image compression-encryption scheme based on compressive sensing (cs) and game of life (gol). Neural Comput. Appl. 32(17), 14113–14141 (2020)

    Google Scholar 

  84. Chai, X., Bi, J., Gan, Z., Liu, X., Zhang, Y., Chen, Y.: Color image compression and encryption scheme based on compressive sensing and double random encryption strategy. Signal Process. 176, 107684 (2020)

  85. Chai, X., Zheng, X., Gan, Z., Han, D., Chen, Y.: An image encryption algorithm based on chaotic system and compressive sensing. Signal Process. 148, 124–144 (2018)

    Google Scholar 

Download references

Acknowledgements

The authors also would like to thank the support from the scientific research project of Hengyang Normal University (No. 18D24), the Science and Technology Plan Project of Hunan Province (No. 2016TP1020), the General Scientific Research Fund of Hunan Provincial Education Department (NO. 19A066).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China

    Xiyu Sun, Zhong Chen, Lujie Wang & Chenchen He

  2. Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China

    Xiyu Sun, Zhong Chen, Lujie Wang & Chenchen He

Authors
  1. Xiyu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lujie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenchen He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Sun, X., Chen, Z., Wang, L. et al. A lossless image compression and encryption algorithm combining JPEG-LS, neural network and hyperchaotic system. Nonlinear Dyn 111, 15445–15475 (2023). https://doi.org/10.1007/s11071-023-08622-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08622-4

Keywords

Search

Navigation