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An image encryption method based on multi-space confusion using hyperchaotic 2D Vincent map derived from optimization benchmark function

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Abstract

A novel multi-space confusion image encryption (MSC-IE) method based on 2D Vincent map is presented in this study. In order to provide a more secure method, the MSC-IE consists of two-stage column and row permutation processing. The 2D Vincent map is derived from the Vincent function, which is an optimization benchmark function. The chaotic performance of the 2D Vincent map is examined through rigorous evaluations such as bifurcation and phase space trajectory diagrams, and Lyapunov exponent, sample entropy, correlation dimension and Kolmogorov entropy which are compared with the state of the art, as well. In the two-stage permutation, the column of the image is scrambled and the rows of the decomposed columns are shuffled, and then the row of the image is scrambled and the columns of the decomposed rows are shuffled. In the diffusion stage, a sequence matrix is converted into an image matrix and diagonally reordered. This reordered image is summed with the permutated image. The result is a completely unrecognizable ciphertext image. The MSC-IE is subjected to reliable cryptanalysis and cyber-attacks, and some results are compared with available reported results. The MSC-IE provides the most secure images due to the superior hyperchaotic performance of the 2D Vincent map.

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Abbreviations

1D:

One-dimensional

PST:

Phase space trajectory

LE:

Lyapunov exponent

SE:

Sample entropy

CD:

Correlation dimension

KE:

Kolmogorov entropy

SC-IE:

Multi-space confusion image encryption

2D-ICM:

2D infinite collapse map

CIEA:

Color image encryption algorithm

CMC:

Cascade modulation couple

FOCM:

Fractional-order chaotic map

PC:

Parameter calculator

SG:

Sequence generator

EP:

Encryption procedure

DP:

Decryption procedure

CC:

Correlation coefficient

NBCR:

Number of bit change rate

LSE:

Local Shannon entropy

NPCR:

Number of pixels changing rate

UACI:

Unified average changing intensity

PSNR:

Peak signal to noise ratio

SPN:

Salt and pepper noise

EPT:

Encryption processing time

NCPB:

Number of cycles per byte

References

  1. Talhaoui, M.Z., Wang, X.: A new fractional one dimensional chaotic map and its application in high-speed image encryption. Inf. Sci. (Ny) 550, 13–26 (2021). https://doi.org/10.1016/J.INS.2020.10.048

    Article  MathSciNet  MATH  Google Scholar 

  2. Wang, X., Gao, S.: Image encryption algorithm based on the matrix semi-tensor product with a compound secret key produced by a Boolean network. Inf. Sci. (Ny) 539, 195–214 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  3. May, R.M.: Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976). https://doi.org/10.1038/261459a0

    Article  MATH  Google Scholar 

  4. Yoshida, T., Mori, H., Shigematsu, H.: Analytic study of chaos of the tent map: band structures, power spectra, and critical behaviors. J. Stat. Phys. 31, 279–308 (1983). https://doi.org/10.1007/BF01011583

    Article  MathSciNet  Google Scholar 

  5. Griffin, J.: The sine map. Retrieved May. 4, 2018 (2013)

    Google Scholar 

  6. Pak, C., Huang, L.: A new color image encryption using combination of the 1D chaotic map. Signal Process. 138, 129–137 (2017). https://doi.org/10.1016/j.sigpro.2017.03.011

    Article  Google Scholar 

  7. Wu, X., Kan, H., Kurths, J.: A new color image encryption scheme based on DNA sequences and multiple improved 1D chaotic maps. Appl. Soft Comput. 37, 24–39 (2015). https://doi.org/10.1016/j.asoc.2015.08.008

    Article  Google Scholar 

  8. Chen, J., Han, F., Qian, W., Yao, Y.-D., Zhu, Z.: Cryptanalysis and improvement in an image encryption scheme using combination of the 1D chaotic map. Nonlinear Dyn. 93, 2399–2413 (2018). https://doi.org/10.1007/s11071-018-4332-9

    Article  Google Scholar 

  9. Hua, Z., Zhou, Y., Pun, C.M., Chen, C.L.P.: 2D sine logistic modulation map for image encryption. Inf. Sci. (Ny) 297, 80–94 (2015). https://doi.org/10.1016/j.ins.2014.11.018

    Article  Google Scholar 

  10. Hua, Z., Zhou, Y.: Image encryption using 2D Logistic-adjusted-Sine map. Inf. Sci. (Ny) 339, 237–253 (2016). https://doi.org/10.1016/j.ins.2016.01.017

    Article  Google Scholar 

  11. Liu, W., Sun, K., Zhu, C.: A fast image encryption algorithm based on chaotic map. Opt. Lasers Eng. (2016). https://doi.org/10.1016/j.optlaseng.2016.03.019

    Article  Google Scholar 

  12. Cao, C., Sun, K., Liu, W.: A novel bit-level image encryption algorithm based on 2D-LICM hyperchaotic map. Signal Process. 143, 122–133 (2018). https://doi.org/10.1016/J.SIGPRO.2017.08.020

    Article  Google Scholar 

  13. Laiphrakpam, D.S., Waikhom, L.S., Brahma, D., Baruah, P., Biswas, S.: Image compression–encryption scheme using SPIHT and chaotic systems. J. Inf. Secur. Appl. 63, 103010 (2021). https://doi.org/10.1016/j.jisa.2021.103010

    Article  Google Scholar 

  14. Xian, Y., Wang, X.: Fractal sorting matrix and its application on chaotic image encryption. Inf. Sci. (Ny) 547, 1154–1169 (2021). https://doi.org/10.1016/J.INS.2020.09.055

    Article  MathSciNet  MATH  Google Scholar 

  15. Ewees, A.A., Abd Elaziz, M., Houssein, E.H.: Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst. Appl. 112, 156–172 (2018). https://doi.org/10.1016/j.eswa.2018.06.023

    Article  Google Scholar 

  16. Cao, W., Mao, Y., Zhou, Y.: Designing a 2D infinite collapse map for image encryption. Signal Process. 171, 107457 (2020)

    Article  Google Scholar 

  17. Hua, Z., Zhu, Z., Chen, Y., Li, Y.: Color image encryption using orthogonal Latin squares and a new 2D chaotic system. Nonlinear Dyn. 104, 4505–4522 (2021). https://doi.org/10.1007/S11071-021-06472-6

    Article  Google Scholar 

  18. Gao, X.: Image encryption algorithm based on 2D hyperchaotic map. Opt. Laser Technol. 142, 107252 (2021). https://doi.org/10.1016/J.OPTLASTEC.2021.107252

    Article  Google Scholar 

  19. Teng, L., Wang, X., Yang, F., Xian, Y.: Color image encryption based on cross 2D hyperchaotic map using combined cycle shift scrambling and selecting diffusion. Nonlinear Dyn. 2021(105), 1859–1876 (2021). https://doi.org/10.1007/S11071-021-06663-1

    Article  Google Scholar 

  20. Sun, J.: 2D-SCMCI hyperchaotic map for image encryption algorithm. IEEE Access. 9, 59313–59327 (2021). https://doi.org/10.1109/ACCESS.2021.3070350

    Article  Google Scholar 

  21. Zhu, L., Jiang, D., Ni, J., Wang, X., Rong, X., Ahmad, M., Chen, Y.: A stable meaningful image encryption scheme using the newly-designed 2D discrete fractional-order chaotic map and Bayesian compressive sensing. Signal Process. 195, 108489 (2022). https://doi.org/10.1016/j.sigpro.2022.108489

    Article  Google Scholar 

  22. Nan, S., Feng, X., Wu, Y., Zhang, H.: Remote sensing image compression and encryption based on block compressive sensing and 2D-LCCCM. Nonlinear Dyn. (2022). https://doi.org/10.1007/s11071-022-07335-4

    Article  Google Scholar 

  23. Teng, L., Wang, X., Xian, Y.: Image encryption algorithm based on a 2D-CLSS hyperchaotic map using simultaneous permutation and diffusion. Inf. Sci. (Ny) 605, 71–85 (2022). https://doi.org/10.1016/j.ins.2022.05.032

    Article  Google Scholar 

  24. Krishnamoorthi, S., Jayapaul, P., Dhanaraj, R.K., Rajasekar, V., Balusamy, B., Islam, S.K.H.: Design of pseudo-random number generator from turbulence padded chaotic map. Nonlinear Dyn. 104, 1627–1643 (2021). https://doi.org/10.1007/s11071-021-06346-x

    Article  Google Scholar 

  25. Hua, Z., Chen, Y., Bao, H., Zhou, Y.: Two-dimensional parametric polynomial chaotic system. IEEE Trans. Syst. Man Cybern. Syst. 52, 4402–4414 (2022). https://doi.org/10.1109/TSMC.2021.3096967

    Article  Google Scholar 

  26. Shir, O.M., Bäck, T.: Niche radius adaptation in the CMA-ES niching algorithm BT—Parallel problem solving from nature—PPSN IX. Presented at the (2006)

  27. Hua, Z., Zhou, Y., Huang, H.: Cosine-transform-based chaotic system for image encryption. Inf. Sci. (Ny) 480, 403–419 (2019). https://doi.org/10.1016/j.ins.2018.12.048

    Article  Google Scholar 

  28. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Circ. Physiol. 278, H2039–H2049 (2000). https://doi.org/10.1152/ajpheart.2000.278.6.H2039

    Article  Google Scholar 

  29. Theiler, J.: Efficient algorithm for estimating the correlation dimension from a set of discrete points. Phys. Rev. A. 36, 4456–4462 (1987). https://doi.org/10.1103/PhysRevA.36.4456

    Article  MathSciNet  Google Scholar 

  30. Grassberger, P., Procaccia, I.: Estimation of the Kolmogorov entropy from a chaotic signal. Phys. Rev. A. 28, 2591–2593 (1983). https://doi.org/10.1103/PhysRevA.28.2591

    Article  Google Scholar 

  31. COVID-19 Chest X-ray dataset Initiative, https://github.com/agchung/Figure1-COVID-chestxray-dataset

  32. Asuni, N., Giachetti, A.: TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms. In: Giachetti, A. (ed.) STAG: Smart Tools & Apps for Graphics (2014). The Eurographics Association (2014)

  33. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proc. ACM Conf. Multimedia (MM). pp. 675–678 (2014)

  34. Rich Franzen: Kodak, http://r0k.us/graphics/kodak/

  35. Alvarez, G., Li, S.: Some basic cryptographic requirements for chaos-based cryptosystems. Int. J. Bifurc. Chaos. 16, 2129–2151 (2006). https://doi.org/10.1142/S0218127406015970

    Article  MathSciNet  MATH  Google Scholar 

  36. Castro, J.C.H., Sierra, J.M., Seznec, A., Izquierdo, A., Ribagorda, A.: The strict avalanche criterion randomness test. Math. Comput. Simul. 68, 1–7 (2005). https://doi.org/10.1016/J.MATCOM.2004.09.001

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhang, X., Zhao, Z., Wang, J.: Chaotic image encryption based on circular substitution box and key stream buffer. Signal Process. Image Commun. 29, 902–913 (2014). https://doi.org/10.1016/j.image.2014.06.012

    Article  Google Scholar 

  38. Naskar, P.K., Bhattacharyya, S., Mahatab, K.C., Dhal, K.G., Chaudhuri, A.: An efficient block-level image encryption scheme based on multi-chaotic maps with DNA encoding. Nonlinear Dyn. 105, 3673–3698 (2021). https://doi.org/10.1007/S11071-021-06761-0

    Article  Google Scholar 

  39. Azam, N.A., Hayat, U., Ayub, M.: A substitution box generator, its analysis, and applications in image encryption. Signal Process. 187, 108144 (2021). https://doi.org/10.1016/J.SIGPRO.2021.108144

    Article  Google Scholar 

  40. Wang, X., Zhang, M.: An image encryption algorithm based on new chaos and diffusion values of a truth table. Inf. Sci. (Ny) 579, 128–149 (2021). https://doi.org/10.1016/J.INS.2021.07.096

    Article  MathSciNet  Google Scholar 

  41. Hu, M., Li, J., Di, X.: Quantum image encryption scheme based on 2D Sine 2-Logistic chaotic map. Nonlinear Dyn. 111, 2815–2839 (2023). https://doi.org/10.1007/s11071-022-07942-1

    Article  Google Scholar 

  42. Wu, Y., Zhou, Y., Saveriades, G., Agaian, S., Noonan, J.P., Natarajan, P.: Local Shannon entropy measure with statistical tests for image randomness. Inf. Sci. (Ny) 222, 323–342 (2013). https://doi.org/10.1016/j.ins.2012.07.049

    Article  MathSciNet  MATH  Google Scholar 

  43. Gao, S., Wu, R., Wang, X., Liu, J., Li, Q., Wang, C., Tang, X.: Asynchronous updating Boolean network encryption algorithm. IEEE Trans. Circuits Syst. Video Technol. (2023). https://doi.org/10.1109/TCSVT.2023.3237136

    Article  Google Scholar 

  44. Yu, F., Gong, X., Li, H., Wang, S.: Differential cryptanalysis of image cipher using block-based scrambling and image filtering. Inf. Sci. (Ny) 554, 145–156 (2021). https://doi.org/10.1016/J.INS.2020.12.037

    Article  MathSciNet  MATH  Google Scholar 

  45. Zhu, L., Song, H., Zhang, X., Yan, M., Zhang, T., Wang, X., Xu, J.: A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding. Signal Process. 175, 107629 (2020). https://doi.org/10.1016/J.SIGPRO.2020.107629

    Article  Google Scholar 

  46. Wu, Y., Noonan, J.P., Agaian, S.: NPCR and UACI randomness tests for image encryption. Cyber J. Multidiscip. J Sci Technol. J. Sel. Areas Telecommun. 1, 31–38 (2011)

    Google Scholar 

  47. Yang, Y.-G., Wang, B.-P., Pei, S.-K., Zhou, Y.-H., Shi, W.-M., Liao, X.: Using M-ary decomposition and virtual bits for visually meaningful image encryption. Inf. Sci. (Ny) 580, 174–201 (2021). https://doi.org/10.1016/J.INS.2021.08.073

    Article  MathSciNet  Google Scholar 

  48. Zhou, S.: A real-time one-time pad DNA-chaos image encryption algorithm based on multiple keys. Opt. Laser Technol. 143, 107359 (2021). https://doi.org/10.1016/J.OPTLASTEC.2021.107359

    Article  Google Scholar 

  49. Khalil, N., Sarhan, A., Alshewimy, M.A.M.: An efficient color/grayscale image encryption scheme based on hybrid chaotic maps. Opt. Laser Technol. 143, 107326 (2021). https://doi.org/10.1016/J.OPTLASTEC.2021.107326

    Article  Google Scholar 

  50. Wang, X., Gao, S.: Image encryption algorithm for synchronously updating Boolean networks based on matrix semi-tensor product theory. Inf. Sci. (Ny) 507, 16–36 (2020). https://doi.org/10.1016/J.INS.2019.08.041

    Article  MathSciNet  MATH  Google Scholar 

  51. Zou, C., Wang, X., Li, H.: Image encryption algorithm with matrix semi-tensor product. Nonlinear Dyn. 105, 859–876 (2021). https://doi.org/10.1007/S11071-021-06542-9

    Article  Google Scholar 

  52. Wu, Y., Zhang, L., Qian, T., Liu, X., Xie, Q.: Content-adaptive image encryption with partial unwinding decomposition. Signal Process. 181, 107911 (2021)

    Article  Google Scholar 

  53. Zhu, H., Ge, J., Qi, W., Zhang, X., Lu, X.: Dynamic analysis and image encryption application of a sinusoidal-polynomial composite chaotic system. Math. Comput. Simul. 198, 188–210 (2022). https://doi.org/10.1016/j.matcom.2022.02.029

    Article  MathSciNet  MATH  Google Scholar 

  54. Dong, Y., Zhao, G., Ma, Y., Pan, Z., Wu, R.: A novel image encryption scheme based on pseudo-random coupled map lattices with hybrid elementary cellular automata. Inf. Sci. (Ny) 593, 121–154 (2022). https://doi.org/10.1016/j.ins.2022.01.031

    Article  Google Scholar 

  55. Wang, X., Guan, N., Yang, J.: Image encryption algorithm with random scrambling based on one-dimensional logistic self-embedding chaotic map. Chaos, Solitons Fractals. 150, 111117 (2021). https://doi.org/10.1016/J.CHAOS.2021.111117

    Article  MathSciNet  Google Scholar 

  56. Bachmann, P.: Analytische Zahlentheorie. , (in German). 2. Leipzig: Teubner. (1894)

  57. Landau, E.: Handbuch der Lehre von der Verteilung der Primzahlen. , (in German). Leipzig: B. G. Teubner. (1909)

  58. Toktas, A., Erkan, U., Toktas, F., Yetgin, Z., Yetgın, Z.: Chaotic map optimization for image encryption using triple objective differential evolution algorithm. IEEE Access. 9, 127814–127832 (2021). https://doi.org/10.1109/ACCESS.2021.3111691

    Article  Google Scholar 

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Erkan, U., Toktas, A., Memiş, S. et al. An image encryption method based on multi-space confusion using hyperchaotic 2D Vincent map derived from optimization benchmark function. Nonlinear Dyn 111, 20377–20405 (2023). https://doi.org/10.1007/s11071-023-08859-z

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