iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S00530-021-00868-5
Image lossless encoding and encryption method of EBCOT Tier1 based on 4D hyperchaos | Multimedia Systems Skip to main content
Log in

Image lossless encoding and encryption method of EBCOT Tier1 based on 4D hyperchaos

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

Abstract

To satisfy requirements of high security and transmission efficiency in application scenarios with high image quality, an image lossless encryption and compression algorithm based on four-dimensional hyperchaos and embedded block coding with optimal truncation (EBCOT) is proposed in this paper. First, according to a character that the amplitude of the high-frequency part of the wavelet coefficient is less than the amplitude of the low-frequency part, an encryption algorithm for the wavelet coefficients is proposed to improve the security while reducing the impact on the compression performance. Second, the bit-plane coding and arithmetic coding in EBCOT Tier1 are embedded with encryption points, and the encryption process and compression process are combined to propose a secure EBCOT Tier1 code, which could further improve the security of the algorithm. Furthermore, this paper proposes a new four-dimensional hyperchaotic system, using Secure Hash Algorithm-256 (SHA-256) to generate initial values of the chaotic system, so that the algorithm could resist known plaintext and selected plaintext attacks. The results of the mean square error of all restored images are 0 and the information entropy of this algorithm is close to the theoretical value. The experimental results show that the algorithm has high security and lossless compression performance.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Vijayvergia, A., Kumar, K.: Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimed. Tools Appl. 80, 28349–28363 (2021)

    Article  Google Scholar 

  2. Vijayvergia, A., Kumar, K.: STAR: rating of reviewS by exploiting variation in emoTions using trAnsfer leaRning framework. In: 2018 Conference on Information and Communication Technology (CICT). IEEE (2018)

  3. Kumar, A., Purohit, K., Kumar, K.: Stock price prediction using recurrent neural network and long short-term memory. In: International Conference on Deep Learning, Artificial Intelligence and Robotics, pp. 153–160. Springer, Cham (2019)

  4. Kumain, S.C., Singh, M., Singh, N., et al.: An efficient Gaussian noise reduction technique for noisy images using optimized filter approach. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) , pp. 243–248. IEEE (2018)

  5. Yan, C., Li, Z., Zhang, Y., et al.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16(4), 122–138 (2020)

    Article  Google Scholar 

  6. Kumar, K., Kumar, A., Bahuguna, A.: D-CAD: deep and crowded anomaly detection. In: Proceedings of the 7th international conference on computer and communication technology, pp. 100–105 (2017)

  7. Solanki, A., Bamrara, R., Kumar, K., et al.: VEDL: a novel video event searching technique using deep learning. Perspective 2(4), 1–10 (2020)

    Google Scholar 

  8. Yan, C., Li, L., Zhang, C., et al.: Cross-Modality Bridging and Knowledge Transferring for Image Understanding. IEEE Trans. Multimed. 21(10), 2675–2685 (2019)

    Article  Google Scholar 

  9. Yan, C., Hao, Y., Li, L., et al.: Task-adaptive attention for image captioning. IEEE Trans. Circuits Syst. Video Technol. 14, 1–9 (2021)

    Google Scholar 

  10. Sharma, S., Kumar, K.: Guess: genetic uses in video encryption with secret sharing. In: Proceedings of 2nd International Conference on Computer Vision and Image Processing, pp. 51–62. Springer, Singapore (2018)

  11. Manupriya, P., Sinha, S., Kumar, K.: V⊕ SEE: Video secret sharing encryption technique. In: 2017 Conference on Information and Communication Technology (CICT), pp. 1–6. IEEE (2017)

  12. Purohit, K., Kumar, A., Upadhyay, M., et al.: Symmetric key generation and distribution using Diffie–Hellman algorithm. In: Soft Computing: Theories and Applications, pp. 135–141. Springer, Singapore (2020)

  13. Krishna, R., Kumar, K.: P-MEC: polynomial congruence based multimedia encryption technique over cloud. IEEE Consum. Electron. Mag. 10, 41–46 (2021)

    Article  Google Scholar 

  14. Koppanati, R.K., Kumar, K., Qamar, S.: E-MOC: an efficient secret sharing model for multimedia on cloud. In: International Conference on Deep Learning, Artificial Intelligence and Robotics, pp. 246–260. Springer, Cham (2019)

  15. Yan, C., Gong, B., Wei, Y., et al.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1445–1451 (2020)

    Article  Google Scholar 

  16. Yan, C., Teng, T., Liu, Y., et al.: Precise no-reference image quality evaluation based on distortion identification. ACM Trans. Multimed. Comput. Commun. Appl. 17, 110–131 (2021)

    Google Scholar 

  17. Liu, X., Tong, X., Wang, Z., et al.: Efficient high nonlinearity S-box generating algorithm based on third-order nonlinear digital filter. Chaos Solitons Fract. 150, 111109 (2021)

    Article  MathSciNet  Google Scholar 

  18. Hu, G., Li, B.: Coupling chaotic system based on unit transform and its applications in image encryption. Signal Process. 178, 107790 (2021)

    Article  Google Scholar 

  19. Behzad, Y.I., Peyman, A., Fardin, A.J.: Digital image scrambling based on a new one-dimensional coupled Sine map. Nonlinear Dyn. 97(4), 2693–2721 (2019)

    Article  Google Scholar 

  20. Koppanati, R.K., Qamar, S., Kumar, K.: SMALL: secure multimedia technique using logistic and LFSR. In: 2018 second international conference on intelligent computing and control systems (ICICCS), pp. 1820–1825. IEEE (2018)

  21. 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 

  22. 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 

  23. Wang, X., Lin, S., Li, Y.: Bit-level image encryption algorithm based on BP neural network and gray code. Multimed. Tools Appl. 80, 11655–11670 (2021)

    Article  Google Scholar 

  24. Musanna, F., Dangwal, D., Kumar, S.: Novel image encryption algorithm using fractional chaos and cellular neural network. J. Ambient Intell. Humaniz. Comput. 6, 1–22 (2021)

    Google Scholar 

  25. Ponuma, R., Amutha, R.: Compressive sensing based image compression-encryption using novel 1D-chaotic map. Multimed. Tools Appl. 77, 19209–19234 (2018)

    Article  Google Scholar 

  26. Chai, X., Bi, J., Gan, Z., et al.: Color image compression and encryption scheme based on compressive sensing and double random encryption strategy. Signal Process. 176, 107684 (2020)

    Article  Google Scholar 

  27. Chen, J.X., Zhang, Y., Qi, L., Fu, C., Xu, L.S.: Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression. Opt. Laser Technol. 99, 238–248 (2018)

    Article  Google Scholar 

  28. Gadicha, A.B., Gupta, V.B.B., Gadicha, V.B., et al.: Multimode approach of data encryption in images through quantum steganography. In: Multidiscip. Approach Mod. Digit. Steganogr., pp. 99–124 (2021)

  29. Xiangjun, Wu., Wang, D., Kurths, J., Kan, H.: A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system. Inf. Sci. 349–350, 137–153 (2016)

    Google Scholar 

  30. Zhang, H., Wang, X.Q., Sun, Y.J., et al.: A novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automata. Signal Process. Image Commun. 84(4), 115829 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Taubman, D., Ordentlich, E., Weinberger, M., et al.: Embedded block coding in JPEG 2000. Signal Process. Image Commun. 17(1), 49–72 (2002)

    Article  Google Scholar 

  33. Khelifi, F., Brahimi, T., Han, J., et al.: Secure and privacy-preserving data sharing in the cloud based on lossless image coding. Signal Process. 148, 91–101 (2018)

    Article  Google Scholar 

  34. Raja, S.P.: Multiscale transform based secured joint efficient medical image compression-encryption using symmetric key cryptogrphy and EBCOT encoding technique. Int. J. Wavelets Multiresolut. Inf. Process. 17(05), 1950034 (2019)

    Article  MathSciNet  Google Scholar 

  35. Qiu, H., Qiu, M., et al.: Lightweight selective encryption for social data protection based on EBCOT coding. IEEE Trans. Comput. Soc. Syst. 7(1), 205–214 (2019)

    Article  Google Scholar 

  36. Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (1993)

    Article  Google Scholar 

  37. Pareek, N.K., Patidar, V., Sud, K.K.: Diffusion-substitution based gray image encryption scheme. Digit. Signal Process. 23(3), 894–901 (2013)

    Article  MathSciNet  Google Scholar 

  38. Sun, S., Guo, Y., Wu, R.: A novel image encryption scheme based on 7D hyperchaotic system and row-column simultaneous swapping. IEEE Access. 7, 28539–28547 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Belazi, A., Abd El-Latif, A.A., Belghith, S.: A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process 128, 155–170 (2016)

    Article  Google Scholar 

  41. Zhu, H., Zhang, X., Yu, H., Zhao, C., Zhu, Z.: An image encryption algorithm based on compound homogeneous hyper-chaotic system. Nonlinear Dyn. 89(1), 61–79 (2017)

    Article  Google Scholar 

  42. Zhang, M., Tong, X.: Joint image encryption and compression scheme based on IWT and SPIHT. Opt. Lasers Eng. 90, 254–274 (2017)

    Article  Google Scholar 

  43. Xie, Y., Tang, X., Sun, M., Zhang, Y.: A lossless image compression algorithm based on classification of LZW. J Image Graph 115(2), 236–241 (2010)

    Google Scholar 

  44. Wang, Y., Luo, L., Xie, Q., et al.: A fast stream cipher based on spatiotemporal chaos. In: 2009 International Symposium on Information Engineering and Electronic Commerce. IEEE, pp. 418–422 (2009)

  45. Wang, B., Zheng, X., Zhou, S., et al.: Encrypting the compressed image by chaotic map and arithmetic coding. Optik 125(20), 6117–6122 (2014)

    Article  Google Scholar 

  46. Lian, S., Sun, J., Wang, Z.: Perceptual cryptography on SPIHT compressed images or videos. In: ICME, pp. 2195–2198 (2004)

  47. Boujelbene, R., Jemaa, Y.B., Zribi, M.: A comparative study of recent improvements in wavelet-based image coding schemes. Multimed. Tools Appl. 78(2), 1649–1683 (2019)

    Article  Google Scholar 

  48. Zhang, Y., Cao, H., Jiang, H., et al.: Mutual information-based context template modeling for bitplane coding in remote sensing image compression. J. Appl. Remote Sens. 10(2), 025011 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the following projects and foundations: the National Natural Science Foundation of China (no. 61902091), project ZR2019MF054 supported by Shandong Provincial Natural Science Foundation and Fundamental Research Funds for Central Universities (HIT.NSRIF.2020099), 2017 Weihai University Co-construction Project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaojun Tong.

Additional information

Communicated by C. Yan.

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

Xiao, Y., Tong, X., Zhang, M. et al. Image lossless encoding and encryption method of EBCOT Tier1 based on 4D hyperchaos. Multimedia Systems 28, 727–748 (2022). https://doi.org/10.1007/s00530-021-00868-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-021-00868-5

Keywords

Navigation