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Multimedia Tools and Applications

, Volume 77, Issue 11, pp 14285–14304 | Cite as

A hybrid scheme for self-adaptive double color-image encryption

  • Fang Han
  • Xiaofeng LiaoEmail author
  • Bo Yang
  • Yushu Zhang
Article

Abstract

Most of existing optical color image encryption schemes have born security risks due to the adoption of linear transform, and data redundancy for the generation of complex image. To settle these problems effectively, a hybrid scheme for self-adaptive double color-image encryption is proposed in this paper. In this scheme, each RGB color component of two secret color images is first compressed and encrypted by 2-D compressive sensing (CS) in which measurement matrices are generated by 1-D compound chaotic systems and further are optimized by steepest descent algorithm to improve image reconstruction effect. Then, the two measured images are regarded as the real part and imaginary part to constitute a complex image, respectively. In the end, the complex image is reencrypted by self-adaptive random phase encoding and discrete fractional random transform (DFrRT) to obtain the final encrypted data. In the process of DFrRT and random phase encoding, the correlations between R, G, B components are adequately utilized. The production of key streams not only depends on the initial values of chaotic systems but also on plaintexts, and the three color components affect each other to enhance the ability against the known plaintext attack. The projection neural network algorithm is adopted to obtain the decryption images. Simulation results also verify the validity and security of the proposed method.

Keywords

Self-adaptive Compressive sensing Fractional random transform Image encryption Compound chaotic systems 

Notes

Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2016 YFB0800601, in part by the National Nature Science Foundation of China under Grant 61472331, in part by the Talents of Science and Technology promote plan, Chongqing Science & Technology Commission.

References

  1. 1.
    Baraniuk RG (2007) Compressive sensing. IEEE Signal Proc Mag 24(4)Google Scholar
  2. 2.
    Candes EJ, Romberg JK, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Commun Pur Appl Math 59(8):1207–1223MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Chen F, Wong K-W, Liao X-F, Xiang T (2014) Period distribution of generalized discrete Arnold cat map. Theor Comput Sci 552:13–25MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chen L, Zhao D (2006) Optical color image encryption by wavelength multiplexing and lensless Fresnel transform holograms. Opt Express 14(19):8552–8560CrossRefGoogle Scholar
  5. 5.
    Chen W, Quan C, Tay CJ (2009) Optical color image encryption based on Arnold transform and interference method. Optical Communications, pp 3680–3685Google Scholar
  6. 6.
    Deepan B, Quan C, Wang Y (2014) Multiple-image encryption by space multiplexing based on compressive sensing and the double-random phase-encoding technique. Appl Opt 53(20):4539–4547CrossRefGoogle Scholar
  7. 7.
    Huang R, Rhee KH, Uchida S (2014) A parallel image encryption method based on compressive sensing. Multimed Tools Appl 72(1):71–93CrossRefGoogle Scholar
  8. 8.
    Huang X, Ye G (2014) An image encryption algorithm based on hyper-chaos and DNA sequence. Multimed Tools Appl 72(1):57–70CrossRefGoogle Scholar
  9. 9.
    Joshi M, Singh K (2007) Color image encryption and decryption using fractional Fourier transform. Opt Commun 279(1):35–42CrossRefGoogle Scholar
  10. 10.
    Liang Y, Liu G, Zhou N-R, Wu J (2015) Color image encryption combining a reality-preserving fractional DCT with chaotic mapping in HSI space. Multimedia Tools Application, pp 1–16Google Scholar
  11. 11.
    Li C-J, Li C-D, Huang T-W (2011) Exponential stability of impulsive high-order Hopfield-type neural networks with delays and reaction-diffusion. Int J Comput Math 88(15):3150–3162MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Li C-J, Yu X-H, Huang T-W, Guo C, He X (2016) A generalized Hopfield network for nonsmooth constrained convex optimization: Lie derivative approach. IEEE T Neur Net Lear 27(2):308–321MathSciNetCrossRefGoogle Scholar
  13. 13.
    Li C-J, Yu X-H, Yu W-W, Huang T-W, Liu Z-W (2016) Distributed event-triggered scheme for economic dispatch in smart grids. IEEE T Ind Informat 12 (5):1775–1785CrossRefGoogle Scholar
  14. 14.
    Li C-J, Zhou X-J, Gao DY (2014) Stable trajectory of logistic map. Nonlinear Dynam 78(1):209–217MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Li H, Wang Y (2011) Double-image encryption based on discrete fractional random transform and chaotic maps. Opt Laser Eng 49(7):753–757CrossRefGoogle Scholar
  16. 16.
    Lin Q, Wong K -W, Chen J (2013) An enhanced variable-length arithmetic coding and encryption scheme using chaotic maps. J Syst Softw 86(5):1384–1389CrossRefGoogle Scholar
  17. 17.
    Liu H, Xiao D, Zhang YS (2015) Securely compressive sensing using double random phase encoding. Optik 126(20):2663–2670CrossRefGoogle Scholar
  18. 18.
    Liu Q, Wang J (2016) L 1-Minimization algorithms for sparse signal reconstruction based on a projection neural network. IEEE T Neur Learn 27(3):698–707CrossRefGoogle Scholar
  19. 19.
    Liu X, Mei W, Du H (2014) Optical image encryption based on compressive sensing and chaos in the fractional Fourier domain. J Mod Opt 61(19):1570–1577CrossRefGoogle Scholar
  20. 20.
    Liu Z, Li Z, Liu W, Wang Y, Liu S (2013) Image encryption algorithm by using fractional Fourier transform and pixel scrambling operation based on double random phase encoding. Opt Laser Eng 51(1):8–14CrossRefGoogle Scholar
  21. 21.
    Liu Z, Liu S (2007) Double image encryption based on iterative fractional Fourier transform. Opt Commun 275(2):324–329CrossRefGoogle Scholar
  22. 22.
    Lui OY, Wong K-W, Chen J, Zhou J (2012) Chaos-based joint compression and encryption algorithm for generating variable length ciphertext. Appl Soft Comput 12(1):125–132CrossRefGoogle Scholar
  23. 23.
    Nishchal NK, Joseph J, Singh K (2004) Securing information using fractional Fourier transform in digital holography. Opt Commun 235(4):253–259CrossRefGoogle Scholar
  24. 24.
    Norouzi B, Seyedzadeh SM, Mirzakuchaki S, Mosavi MR (2015) A novel image encryption based on row-column, masking and main diffusion processes with hyper chaos. Multimed Tools Appl 74(3):781–811CrossRefGoogle Scholar
  25. 25.
    Rawat N, Kumar R, Lee BG (2014) Implementing compressive fractional Fourier transformation with iterative kernel steering regression in double random phase encoding. Optik-Int J Light Electron Opt 125(18):5414–5417CrossRefGoogle Scholar
  26. 26.
    Sato D (2015) Security evaluation for leakage of a fingerprint image from USB-based fingerprint authentication systems. Proc IEICE 115(117):39–44Google Scholar
  27. 27.
    Sui L, Lu H, Wang Z, Sun Q (2014) Double-image encryption using discrete fractional random transform and logistic maps. Opt Laser Eng 56:1–12CrossRefGoogle Scholar
  28. 28.
    Tao R, Lang J, Wang Y (2008) Optical image encryption based on the multiple-parameter fractional Fourier transform. Opt Lett 33(6):581–583CrossRefGoogle Scholar
  29. 29.
    Wen J, Li D, Zhu F (2015) Stable recovery of sparse signals via L p-minimization. Appl Comput Harmon A 38(1):161–176CrossRefzbMATHGoogle Scholar
  30. 30.
    Wen J, Zhou Z, Wang J, Tang X-H, Qun M (2016) A sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit. IEEE T Appl SuperconGoogle Scholar
  31. 31.
    Wen J, Zhu X, Li D (2013) Improved bounds on restricted isometry constant for orthogonal matching pursuit. Electron Lett 49(23):1487–1489CrossRefGoogle Scholar
  32. 32.
    Zhang L, Liao X-F, Wang X (2005) An image encryption approach based on chaotic maps. Chaos Solition Fract 24(3):759–765MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Zhang S, Gao T (2015) An image encryption scheme based on DNA coding and permutation of hyper-image. Multimed Tools Appl 2015:1–14Google Scholar
  34. 34.
    Zhang Y -S, Xiao D (2013) Double optical image encryption using discrete Chirikov standard map and chaos-based fractional random transform. Opt Laser Eng 51(4):472–480CrossRefGoogle Scholar
  35. 35.
    Zhang Y-S, Zhang L-Y, Zhou J, Liu L, Chen F, He X (2016) A review of compressive sensing in information security field. IEEE Access 4:2507–2519CrossRefGoogle Scholar
  36. 36.
    Zhang Y-S, Zhou J, Chen F, Zhang L-Y, Wong K-W, He X (2016) Embedding cryptographic features in compressive sensing. Neurocomputing 205:472–480CrossRefGoogle Scholar
  37. 37.
    Zhang Y, Zhang L-Y (2015) Exploiting random convolution and random subsampling for image encryption and compression. Electron Lett 51(20):1572–1574CrossRefGoogle Scholar
  38. 38.
    Zhou Y-C, Bao L, Chen C-L (2014) A new 1D chaotic system for image encryption. Signal Process 97:172–182CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information EngineeringSouthwest UniversityChongqingChina

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