Skip to main content
Log in

Permutation-based special linear transforms with application in quantum image encryption algorithm

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

A cryptographic system based on substitution–permutation network involves various linear and affine transforms in order to create diffusion in the ciphertext. In this paper, special linear unitary transforms associated with permutations of n distinct objects are designed. These transforms are composed of n components each of which is a controlled NOT operation. The domain of special linear transforms is the \(2^n\)-dimensional Hilbert space \({{\mathbb {H}}}^{\otimes n}\), and these transforms define bijection from the set of computational basis onto itself. The latter characteristic of these transforms has enabled us to propose an efficient quantum image scrambling strategy. The application of special linear transform on the quantum state, which represents pixels positional information, results in the scrambled quantum state of positional information. On the other hand, its application on quantum state representing pixels value produces the encrypted quantum state. Accordingly, an efficient quantum image encryption algorithm based on special linear transforms and Chen chaotic dynamical system is presented for the novel quantum representation of color digital images (NCQI) model. Firstly, a selected pair of special linear transforms is employed to scramble the quantum image state of the original image. Then, the scrambled image is processed under the controlled special linear transforms determined by the values of three sequences generated from Chen chaotic system. The objective of this part is to encrypt color information of red, green and blue layers of the image. For an image of size \(2^n \times 2^n\), the time complexity of the proposed quantum image scrambling method is 2n while the time complexity of the proposed quantum image encryption algorithm is \(O(2^{2n})\), which is linear in the size of image. Finally, the simulation experiments are performed in order to measure the strength of proposed encryption algorithm. It is evident from the analyses of the simulation results that the newly proposed algorithm is fast, secure and reliable.

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

Access this article

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

Similar content being viewed by others

References

  1. Nielsen, M.A., Chuang, I.: Quantum computation and quantum information. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  2. Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21(6), 467–488 (1982)

    Article  MathSciNet  Google Scholar 

  3. Deutsch, D.: Quantum theory, the church-turing principle and the universal quantum computer. Proc. R. Soc. Lond. A 400(1818), 97–117 (1985)

    Article  ADS  MathSciNet  Google Scholar 

  4. Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: 35th Annual Symposium on Foundations of Computer Science, 1994 Proceedings, pp. 124–134. IEEE (1994)

  5. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp. 212–219. ACM (1996)

  6. Venegas-Andraca, S.E., Bose, S.: Storing, processing, and retrieving an image using quantum mechanics. In: Quantum Information and Computation, vol. 5105, pp. 137–148. International Society for Optics and Photonics (2003)

  7. Latorre, J.I.: Image compression and entanglement. arXiv preprint arXiv:quant-ph/0510031 (2005)

  8. Venegas-Andraca, S.E., Ball, J.L.: Processing images in entangled quantum systems. Quantum Inf. Process. 9(1), 1–11 (2010)

    Article  MathSciNet  Google Scholar 

  9. Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quantum Inf. Process. 10(1), 63–84 (2011)

    Article  MathSciNet  Google Scholar 

  10. Zhang, Y., Kai, L., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013)

    Article  ADS  MathSciNet  Google Scholar 

  11. Zhang, Y., Kai, L., Gao, Y., Kai, X.: A novel quantum representation for log-polar images. Quantum Inf. Process. 12(9), 3103–3126 (2013)

    Article  ADS  MathSciNet  Google Scholar 

  12. Sun, B., Iliyasu, A.M., Yan, F., Dong, F., Hirota, K.: An RGB multi-channel representation for images on quantum computers. J. Adv. Comput. Intell. Intell. Inform. 17(3), 404–417 (2013)

    Article  Google Scholar 

  13. Sang, J., Wang, S., Li, Q.: A novel quantum representation of color digital images. Quantum Inf. Process. 16(2), 42 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  14. Khan, R.A.: An improved flexible representation of quantum images. Quantum Inf. Process. 18(7), 201 (2019)

    Article  ADS  MathSciNet  Google Scholar 

  15. Vedral, V., Barenco, A., Ekert, A.: Quantum networks for elementary arithmetic operations. Phys. Rev. A 54(1), 147 (1996)

    Article  ADS  MathSciNet  Google Scholar 

  16. Jiang, N., Wen-Ya, W., Wang, L.: The quantum realization of Arnold and Fibonacci image scrambling. Quantum Inf. Process. 13(5), 1223–1236 (2014)

    Article  ADS  MathSciNet  Google Scholar 

  17. Jiang, N., Wang, L.: Analysis and improvement of the quantum Arnold image scrambling. Quantum Inf. Process. 13(7), 1545–1551 (2014)

    Article  ADS  MathSciNet  Google Scholar 

  18. Zhou, N.R., Hua, T.X., Gong, L.H., Pei, D.J., Liao, Q.H.: Quantum image encryption based on generalized arnold transform and double random-phase encoding. Quantum Inf. Process. 14(4), 1193–1213 (2015)

    Article  ADS  MathSciNet  Google Scholar 

  19. Zhou, R.-G., Sun, Y.-J., Fan, P.: Quantum image gray-code and bit-plane scrambling. Quantum Inf. Process. 14(5), 1717–1734 (2015)

    Article  ADS  MathSciNet  Google Scholar 

  20. Liang, H.-R., Tao, X.-Y., Zhou, N.-R.: Quantum image encryption based on generalized affine transform and logistic map. Quantum Inf. Process. 15(7), 2701–2724 (2016)

    Article  ADS  MathSciNet  Google Scholar 

  21. Zhou, N., Yiqun, H., Gong, L., Li, G.: Quantum image encryption scheme with iterative generalized Arnold transforms and quantum image cycle shift operations. Quantum Inf. Process. 16(6), 164 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  22. Zhou, N., Yan, X., Liang, H., Tao, X., Li, G.: Multi-image encryption scheme based on quantum 3d Arnold transform and scaled Zhongtang chaotic system. Quantum Inf. Process. 17(12), 338 (2018)

    Article  ADS  Google Scholar 

  23. Li, P., Zhao, Y.: A simple encryption algorithm for quantum color image. Int. J. Theor. Phys. 56(6), 1961–1982 (2017)

    Article  MathSciNet  Google Scholar 

  24. Li, X.-Z., Chen, W.-W., Wang, Y.-Q.: Quantum image compression-encryption scheme based on quantum discrete cosine transform. Int. J. Theor. Phys. 57(9), 2904–2919 (2018)

    Article  Google Scholar 

  25. Wang, J., Geng, Y.-C., Han, L., Liu, J.-Q.: Quantum image encryption algorithm based on quantum key image. Int. J. Theor. Phys. 58(1), 308–322 (2019)

    Article  Google Scholar 

  26. Heidari, S., Vafaei, M., Houshmand, M., Tabatabaey-Mashadi, N.: A dual quantum image scrambling method. Quantum Inf. Process. 18(1), 9 (2019)

    Article  ADS  Google Scholar 

  27. Jiang, N., Dong, X., Hu, H., Ji, Z., Zhang, W.: Quantum image encryption based on Henon mapping. Int. J. Theor. Phys. 58, 1–13 (2019)

    Article  MathSciNet  Google Scholar 

  28. Tan, R.-C., Lei, T., Zhao, Q.-M., Gong, L.-H., Zhou, Z.-H.: Quantum color image encryption algorithm based on a hyper-chaotic system and quantum Fourier transform. Int. J. Theor. Phys. 55(12), 5368–5384 (2016)

    Article  Google Scholar 

  29. Ran, Q., Wang, L., Ma, J., Tan, L., Siyuan, Y.: A quantum color image encryption scheme based on coupled hyper-chaotic Lorenz system with three impulse injections. Quantum Inf. Process. 17(8), 188 (2018)

    Article  ADS  MathSciNet  Google Scholar 

  30. Chen, G., Ueta, T.: Yet another chaotic attractor. Int. J. Bifurc. Chaos 9(07), 1465–1466 (1999)

    Article  MathSciNet  Google Scholar 

  31. Yang, Y.-G., Jia, X., Sun, S.-J., Pan, Q.-X.: Quantum cryptographic algorithm for color images using quantum Fourier transform and double random-phase encoding. Inf. Sci. 277, 445–457 (2014)

    Article  Google Scholar 

  32. Wang, L., Song, H., Liu, P.: A novel hybrid color image encryption algorithm using two complex chaotic systems. Opt. Lasers Eng. 77, 118–125 (2016)

    Article  Google Scholar 

  33. Yue, W., 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)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amer Rasheed.

Ethics declarations

Conflict of interest

We have no conflict of interest to declare.

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

Khan, M., Rasheed, A. Permutation-based special linear transforms with application in quantum image encryption algorithm. Quantum Inf Process 18, 298 (2019). https://doi.org/10.1007/s11128-019-2410-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-019-2410-7

Keywords

Navigation