Skip to main content
SpringerLink
Log in

Implementation of a quantum image watermarking scheme using NEQR on IBM quantum experience

  • Published:
Quantum Information Processing Aims and scope Submit manuscript

Abstract

Recently, several quantum image watermarking (QIW) schemes have been introduced, but they cannot be implemented in today’s quantum systems due to lack of sufficient number of qubits of these systems. Enhancing the quantum transmitter and receiver circuits of these QIW schemes by reducing their number of qubits without increasing their run-time complexity is highly desired. Therefore, in this paper, a new quantum transmitter and receiver circuit for one of the existing QIW schemes which uses the NEQR quantum image representation is designed. It uses the IBM quantum experience reset operation and our proposed multi-controlled NOT quantum gate named MCNOT-R. Moreover, the complexity analysis and simulation results demonstrate the proposed quantum transmitter and receiver circuits achieve lower circuit complexity in terms of the number of qubits than those of the other quantum circuits for implementing the same watermarking scheme. Furthermore, simulation results show that proposed quantum circuits provide admissible visual quality.

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

Similar content being viewed by others

Data availability

The authors declare they do not have any data to declare.

References

  1. Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Donkor, E., Pirich, A.R., Brandt, H.E. (eds.) Quantum Inf. Comput., vol. 5105, pp. 137–147. SPIE, Orlando (2003). https://doi.org/10.1117/12.485960. International Society for Optics and Photonics

  2. Latorre, J.I.: Image compression and entanglement (2005) arXiv:quant-ph/0510031 [quant-ph]

  3. 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, 63–84 (2011). https://doi.org/10.1007/s11128-010-0177-y

    Article  MathSciNet  MATH  Google Scholar 

  4. Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12, 2833–2860 (2013). https://doi.org/10.1007/s11128-013-0567-z

    Article  ADS  MathSciNet  MATH  Google Scholar 

  5. Jiang, N., Wang, L.: Quantum image scaling using nearest neighbor interpolation. Quantum Inf. Process. 14, 1559–1571 (2015). https://doi.org/10.1007/s11128-014-0841-8

    Article  ADS  MathSciNet  MATH  Google Scholar 

  6. Li, H.-S., Fan, P., Xia, H.-Y., Peng, H., Song, S.: Quantum implementation circuits of quantum signal representation and type conversion. IEEE Trans. Circuits Syst.-I Regul. Pap. 66(1), 341–354 (2019). https://doi.org/10.1109/TCSI.2018.2853655

    Article  Google Scholar 

  7. Li, P., Liu, X.: Color image representation model and its application based on an improved FRQI. Int. J. Quantum Inf. 16(1), 1850005 (2018). https://doi.org/10.1142/S0219749918500053

    Article  MATH  Google Scholar 

  8. Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Strategies for designing geometric transformations on quantum images. Theor. Comput. Sci. 412(15), 1406–1418 (2011). https://doi.org/10.1016/j.tcs.2010.11.029

    Article  MathSciNet  MATH  Google Scholar 

  9. Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.: Efficient color transformations on quantum images. JACIII 15(6), 698–706 (2011). https://doi.org/10.20965/jaciii.2011.p0698

    Article  Google Scholar 

  10. Zhou, N.-R., Huang, L.-X., Gong, L.-H., Zeng, Q.-W.: Novel quantum image compression and encryption algorithm based on DQWT and 3D hyper-chaotic Henon map. Quantum Inf. Process. 19, 284 (2020). https://doi.org/10.1007/s11128-020-02794-3

    Article  ADS  MathSciNet  Google Scholar 

  11. Dai, J.-Y., Ma, Y., Zhou, N.-R.: Quantum multi-image compression-encryption scheme based on quantum discrete cosine transform and 4D hyper-chaotic Henon map. Quantum Inf. Process. 20, 246 (2021). https://doi.org/10.1007/s11128-021-03187-w

    Article  ADS  MathSciNet  Google Scholar 

  12. Yan, F., Iliyasu, A.M., Le, P.Q.: Quantum image processing: a review of advances in its security technologies. Int. J. Quantum Inf. 15(3), 1730001 (2017). https://doi.org/10.1142/S0219749917300017

    Article  MathSciNet  MATH  Google Scholar 

  13. Luo, G., Zhou, R.-G., Luo, J., Hu, W., Zhou, Y., Ian, H.: Adaptive LSB quantum watermarking method using tri-way pixel value differencing. Quantum Inf. Process. 18(49), 1–20 (2019). https://doi.org/10.1007/s11128-018-2165-6

    Article  ADS  MathSciNet  MATH  Google Scholar 

  14. Jiang, N., Zhao, N., Wang, L.: LSB based quantum image steganography algorithm. Int. J. Theor. Phys. 55, 107–123 (2016). https://doi.org/10.1007/s10773-015-2640-0

    Article  MATH  Google Scholar 

  15. Heidari, S., Naseri, M.: A novel LSB based quantum watermarking. Int. J. Theor. Phys. 55, 4205–4218 (2016). https://doi.org/10.1007/s10773-016-3046-3

    Article  MATH  Google Scholar 

  16. Miyake, S., Nakamae, K.: A quantum watermarking scheme using simple and small-scale quantum circuits. Quantum Inf. Process. 15, 1849–1864 (2016). https://doi.org/10.1007/s11128-016-1260-9

    Article  ADS  MathSciNet  MATH  Google Scholar 

  17. Li, P., Zhao, Y., Xiao, H., Cao, M.: An improved quantum watermarking scheme using small-scale quantum circuits and color scrambling. Quantum Inf. Process. (2017). https://doi.org/10.1007/s11128-017-1577-z

    Article  MATH  Google Scholar 

  18. Zhou, R.-G., Hu, W., Fan, P.: Quantum watermarking scheme through Arnold scrambling and LSB steganography. Quantum Inf. Process. 16(212), 1–21 (2017). https://doi.org/10.1007/s11128-017-1640-9

    Article  ADS  MathSciNet  MATH  Google Scholar 

  19. Zhou, R.-G., Hu, W., Fan, P., Luo, G.: Quantum color image watermarking based on Arnold transformation and LSB steganography. Int. J. Quantum Inf. 16(3), 1850021 (2018). https://doi.org/10.1142/S0219749918500211

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhou, R.-G., Luo, J., Liu, X., Zhu, C., Wei, L., Zhang, X.: A novel quantum image steganography scheme based on LSB. Int. J. Theor. Phys. 57, 1848–1863 (2018). https://doi.org/10.1007/s10773-018-3710-x

    Article  MathSciNet  MATH  Google Scholar 

  21. Luo, G., Zhou, R.-G., Hu, W., Luo, J., Liu, X., Ian, H.: Enhanced least significant qubit watermarking scheme for quantum images. Quantum Inf Process. 17, 299 (2018). https://doi.org/10.1007/s11128-018-2075-7

    Article  ADS  MATH  Google Scholar 

  22. Hu, W., Zhou, R.-G., Luo, J., Liu, B.: LSBs-based quantum color images watermarking algorithm in edge region. Quantum Inf. Process. 18(16), 1–27 (2019). https://doi.org/10.1007/s11128-018-2138-9

    Article  MATH  Google Scholar 

  23. Luo, J., Zhou, R.-G., Luo, G., Li, Y., Liu, G.: Traceable quantum steganography scheme based on pixel value differencing. Sci. Rep. 9(15134), 1–12 (2019). https://doi.org/10.1038/s41598-019-51598-8

    Article  ADS  Google Scholar 

  24. Zeng, Q.-W., Wen, Z.-Y., Fu, J.-F., Zhou, N.-R.: Quantum watermark algorithm based on maximum pixel difference and tent map. Int. J. Theor. Phys. 60, 3306–3333 (2021). https://doi.org/10.1007/s10773-021-04909-7

    Article  MathSciNet  MATH  Google Scholar 

  25. Atta, R., Ghanbari, M.: A high payload steganography mechanism based on wavelet packet transformation and neutrosophic set. J. Vis. Commun. Image Represent. 53, 42–54 (2018). https://doi.org/10.1016/j.jvcir.2018.03.009

    Article  Google Scholar 

  26. Atta, R., Ghanbari, M., Elnahry, I.: Advanced image steganography based on exploiting modification direction and neutrosophic set. Multimed. Tools Appl. 80, 21751–21769 (2021). https://doi.org/10.1007/s11042-021-10784-5

    Article  Google Scholar 

  27. Wu, D.-C., Tsai, W.-H.: A steganographic method for images by pixel value differncing. Pattern Recognit. Lett. 24, 1613–1626 (2003). https://doi.org/10.1016/S0167-8655(02)00402-6

    Article  ADS  MATH  Google Scholar 

  28. IBM Quantum Experience. Accessed on: Apr. 4, (2021) https://quantum-computing.ibm.com

  29. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information, Anniversary Cambridge University Press, Cambrige (2010)

    MATH  Google Scholar 

  30. Barenco, A., Bennett, C.H., Cleve, R., DiVincenzo, D.P., Margolus, N., Shor, P., Sleator, T., Smolin, J.A., Weinfurter, H.: Elementary gates for quantum computation. Phys. Rev. A 52, 3457–3467 (1995). https://doi.org/10.1103/PhysRevA.52.3457

    Article  ADS  Google Scholar 

  31. Zhou, R.-G., Hu, W., Luo, G., Liu, X., Fan, P.: Quantum realization of the nearest neighbor value interpolation method for INEQR. Quantum Inf. Process. 17(166), 1–37 (2018). https://doi.org/10.1007/s11128-018-1921-y

    Article  MathSciNet  MATH  Google Scholar 

  32. Xu, X., Xiao, F., Zhang, J., Chen, H.: Application of dichotomy in the decomposition of multi-line quantum logic gate. J. Southeast Univ. 5, 928–931 (2010). https://doi.org/10.3969/j.issn.1001-0505.2010.05.009

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang, D., Liu, Z., Zhu, W., Li, S.: Design of quantum comparator based on extended general Toffoli gates with multiple targets. Comput. Sci. 39(9), 302–306 (2012)

    Google Scholar 

  34. How to measure and reset a qubit in the middle of a circuit execution. Accessed 4 Apr, (2021) https://www.ibm.com/blogs/research/2021/02/quantum-mid-circuit-measurement

  35. Córcoles, A.D., Takita, M., Inoue, K., Lekuch, S., Minev, Z.K., Chow, J.M., Gambetta, J.M.: Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits. Phys. Rev. Lett. 127, 100501 (2021). https://doi.org/10.1103/PhysRevLett.127.100501

    Article  ADS  Google Scholar 

  36. Lee, J.-S., Chung, Y., Kim, J., Lee, S.: A practical method of constructing quantum combinational logic circuits (1999) arXiv:quant-ph/9911053 [quant-ph]

  37. The USC-SIPI Image Database. Accessed 4 Apr (2021) https://sipi.usc.edu/database

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Shahab Iranmanesh & Mohammad Ghanbari

  2. Electrical Engineering Department, Port Said University, Port Said, 42523, Egypt

    Randa Atta

  3. School of Computer Science and Electronic Systems Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Mohammad Ghanbari

Authors
  1. Shahab Iranmanesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Randa Atta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Ghanbari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Randa Atta.

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

Iranmanesh, S., Atta, R. & Ghanbari, M. Implementation of a quantum image watermarking scheme using NEQR on IBM quantum experience. Quantum Inf Process 21, 194 (2022). https://doi.org/10.1007/s11128-022-03530-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11128-022-03530-9

Keywords

Search

Navigation