Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Adaptive encryption of digital images based on lifting wavelet optimization

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A large number of digital pictures have many security threats in the transmission on the Internet, and various encryption technologies for images have been proposed. The existing image encryption technology has a simple structure and a small key space, which cannot meet the requirement for image information security at this stage. In order to improve the effectiveness of image encryption and enhance the adaptive ability of image encryption, based on the theory of wavelet transform, chaos theory and adaptive encryption, this paper designs the overall framework of image encryption, and proposes a digital image adaptive encryption method based on lifting wavelet optimization. Firstly, the framework of image frequency domain adaptive encryption algorithm is designed, wavelet transform based on lifting is studied and analyzed, and lifting wavelet optimization method based on improved threshold method and particle swarm algorithm is given to improve the adaptability to image decomposition, so that the frequency domain coefficients obtained after wavelet decomposition can better represent the image content, thus making frequency domain encryption more targeted. Chaos mapping is used to encrypt the pixel gray value and scramble the pixel position of the decomposed low frequency coefficients of the image, thus achieving good image adaptive encryption.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Antonini M et al (1992) Image coding using wavelet transforms [J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 1(2):205–220

    Article  Google Scholar 

  2. Brodatz image database http://www.humanscan.de/support/downloads/facedb.php

  3. Chai X (2017) An image encryption algorithm based on bit level Brownian motion and new chaotic systems [J]. Multimedia Tools & Applications 76(1):1159–1175

    Article  Google Scholar 

  4. Daubechies I et al (1998) Factoring wavelet transforms into lifting steps [J]. Journal of Fourier Analysis & Applications 4(3):247–269

    Article  MathSciNet  MATH  Google Scholar 

  5. Hu H, Ye H (2016) Adaptive wavelet bases and image denoising.[J]. Communication on Applied Mathematics & Computation 30(01):156–164

    MathSciNet  MATH  Google Scholar 

  6. Lewis AS et al (2002) Image compression using the 2-D wavelet transforms [J]. IEEE Trans Image Process 1(2):244–250

    Article  Google Scholar 

  7. Li X et al (2011) Research on power information encryption algorithm based on composite chaotic system in wavelet transform domain [J]. Procedia Engineering 15:2118–2122

    Article  Google Scholar 

  8. Lv X, Liao X, Yang B (2018) A novel scheme for simultaneous image compression and encryption based on wavelet packet transform and multi-chaotic systems [J]. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6013-6

  9. Ma X, Zhou X, An FP (2018) Fast bi-dimensional empirical mode decomposition (BEMD) based on variable neighborhood window method [J]. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6629-6

  10. Mallat S (2009) A wavelet tour of signal processing [J] 31(3):83–85

    Google Scholar 

  11. Mallat S (2009) An efficient image representation for multiscale analysis [J]. Biometrics 65(65):143–151

    MathSciNet  Google Scholar 

  12. Ren K, Zhang K, Xie B (2012) Adaptive blind video watermarking algorithm based on low frequency coefficients in wavelet domain [J]. Video Engineering 89(62):131–149

    Google Scholar 

  13. Sweldens W (1996) The lifting scheme: a custom-design construction of biorthogonal wavelets [J]. Applied & Computational Harmonic Analysis 3(2):186–200

    Article  MathSciNet  MATH  Google Scholar 

  14. Tang P, Zhang J, Wang X et al (2017) Learning extremely shared middle-level image representation for scene classification. Knowl Inf Syst 52(2):509–530

    Article  Google Scholar 

  15. Tang L, Qian J, Li L et al (2017) Multimodal medical image fusion based on discrete T chebichef moments and pulse coupled neural network [J]. International Journal of Imaging Systems & Technology 27(1):57–65

    Article  Google Scholar 

  16. Tanmoy R, Tshilidzi M, Snehashish C (2018) Precise detection of speech endpoints dynamically: a wavelet convolution based approach [J]. Commun Nonlinear Sci Numer Simul. https://doi.org/10.1016/j.cnsns.2018.07.008

  17. Technometrics (2000) Logistic regression using the SAS® system [J]. Technometrics (3):323–324

  18. Uehara T, Safavi-Naini R, Ogunbona P (2000) Securing wavelet compression with random permutations[C]. Faculty of Informatics-papers

  19. Wang YM, Li T (2012) Digital watermarking based on chaotic encryption and wavelet transformation [J]. Adv Mater Res 403-408:3054–3058

    Article  Google Scholar 

  20. Wang X, Wang P, Zhang P et al (2013) A norm-space, adaptive, and blind audio watermarking algorithm by discrete wavelet transform [J]. Signal Process 93(4):913–922

    Article  Google Scholar 

  21. Wu Y, Tong Y, Zhu X et al (2018) NOSEP: nonoverlapping sequence pattern mining with gap constraints. IEEE Transactions on Cybernetics 48(10):2809–2822

    Article  Google Scholar 

  22. Xingmin M, Shenggang X, Fengping A, Fuhong L (2018) A novel real-time image restoration algorithm in edge computing [J]. Wirel Commun Mob Comput 9(4):1441–1456

    Google Scholar 

  23. Xue Y, Chen WN, Gu T et al (2017) Set-based discrete particle swarm optimization based on decomposition for permutation-based multiobjective combinatorial optimization problems [J]. IEEE Transactions on Cybernetics 48(7):2139–2153

    Google Scholar 

  24. Yang B, Li S (2010) Multifocus image fusion and restoration with sparse representation [J]. IEEE Transactions on Instrumentation & Measurement 59(4):884–892

    Article  Google Scholar 

  25. Ye L, Wang J, Fan J et al (2016) Image encryption algorithm based on chaotic system and dynamic S-boxes composed of DNA sequences [J]. Multimedia Tools & Applications 75(8):4363–4382

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Jian Wang, Wenyuan Liu & Shuai Zhang

  2. The First Hospital of Qinhuangdao, Qinhuangdao, 066000, Hebei, China

    Jian Wang

  3. Hebei Normal University of Science &Technology, Qinhuangdao, 066004, Hebei, China

    Shuai Zhang

Authors
  1. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenyuan Liu.

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

Wang, J., Liu, W. & Zhang, S. Adaptive encryption of digital images based on lifting wavelet optimization. Multimed Tools Appl 79, 9363–9386 (2020). https://doi.org/10.1007/s11042-019-7704-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7704-3

Keywords

Search

Navigation