Skip to main content
Log in

A novel chaos-based approach in conjunction with MR-SVD and pairing function for generating visually meaningful cipher images

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

Abstract

For secure transmission of digital images, existing cryptographic algorithms transform images into a noise-like appearance. One obvious inference that an adversary could draw is that the noise-like texture is a cipher, prompting to apply classical cryptanalysis. This article proposes an efficient algorithm that produces a visually coherent and meaningful cipher image to bypass the cryptanalysis. In the partial-encryption phase, the Arnold-3D map drives the permutation mechanism, whereas the implementation of the diffusion phase is done by harnessing the iterates of the chaotic Logistic map. The partial cipher is compressed using a Cantor-like pairing function that does a 4 to 1 pixel encoding to facilitate embedding. The embedding phase is implemented in the spatial domain by applying Multi-resolution singular value decomposition on the reference image and replacing the vertical, horizontal, diagonal sub-band with the encoded cipher. The encoding of pixels facilitates the transmission of the visually meaningful cipher image to be of the same size as the original image rather than four-times the original image as reported in earlier schemes. Simulation results confirm the security of the scheme.

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

Similar content being viewed by others

Notes

  1. Since the maximum value of a pixel in the partial cipher can be 255 so the maximum outcome of the first pairing function can be 65535 and the maximum from the second can be 4.294967295000000e + 09. To accommodate the values in a desirable range, we divide the whole vector matrix W1 by a digitizing factor DF = 108.

References

  1. Akhbari B, Ghaemmaghami S (2005) Watermarking of still images using multiresolution singular value decomposition. In: 2005. ISPACS 2005. Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, pp 325–328

  2. Ashino R, Morimoto A, Nagase M, Vaillancourt R (2005) Comparing multiresolution svd with other methods for image compression. In: Advances In Analysis. World Scientific, pp 457–470

  3. Ashino R, Morimoto A, Vaillancourt R (2005) Image compression with multiresolution singular value decomposition and other methods. Math Comput Model 41(6-7):773–790

    MathSciNet  MATH  Google Scholar 

  4. Bao L, Zhou Y (2015) Image encryption: Generating visually meaningful encrypted images. Inf Sci 324:197–207

    MathSciNet  MATH  Google Scholar 

  5. Baptista M S (1998) Cryptography with chaos. Phys Lett A 240 (1-2):50–54

    MathSciNet  MATH  Google Scholar 

  6. Cantor G (1874) Ueber eine eigenschaft des inbegriffs aller reellen algebraischen zahlen. J Reine Angewan Math 77:258–262

    MathSciNet  MATH  Google Scholar 

  7. Chai X, Gan Z, Chen Y, Zhang Y (2017) A visually secure image encryption scheme based on compressive sensing. Signal Process 134:35–51

    Google Scholar 

  8. Chen M, Zhang Z, Cai Z, Pan Y (2016) A novel image encryption method based on fractional fourier transform and odd-even quantification. In: Eighth International Conference on Digital Image Processing (ICDIP 2016). International Society for Optics and Photonics, vol 10033, pp 1003332

  9. Dube S, Sharma K (2019) Hybrid approach to enhance contrast of image for forensic investigation using segmented histogram. Int J Adv Intell Parad 13(1-2):43–66

    Google Scholar 

  10. Feigenbaum MJ (1978) . J Stat Phys 19:25

    Google Scholar 

  11. Franċois M, Grosges T, Barchiesi D, Erra R (2012) Image encryption algorithm based on a chaotic iterative process. Appl Math 3(12):1910

    Google Scholar 

  12. Fridrich J (1998) Symmetric ciphers based on two-dimensional chaotic maps. Int J Bifurcat Chaos 8(6):1259–1284

    MathSciNet  MATH  Google Scholar 

  13. Fu C, Lin B-b, Miao Y-s, Liu X, Chen J-j (2011) A novel chaos-based bit-level permutation scheme for digital image encryption. Opt Commun 284(23):5415–5423

    Google Scholar 

  14. Golub GH, Reinsch C (1970) Singular value decomposition and least squares solutions. Numer Math 14(5):403–420

    MathSciNet  MATH  Google Scholar 

  15. Guo C, Liu Si, Sheridan JT (2014) Optical double image encryption employing a pseudo image technique in the fourier domain. Opt Commun 321:61–72

    Google Scholar 

  16. Haralick RM, Shapiro LG (1992) Computer and robot vision, vol 1. Addison-wesley Reading

  17. Hua Z, Zhou Y, Pun C M, Chen CLP (2015) 2D Sine Logistic modulation map for image encryption. Inf Sci 297:80–94

    Google Scholar 

  18. Kakarala R, Ogunbona PO (2001) Signal analysis using a multiresolution form of the singular value decomposition. IEEE Trans Image Process 10(5):724–735

    MathSciNet  MATH  Google Scholar 

  19. Kanso A, Ghebleh M (2017) An algorithm for encryption of secret images into meaningful images. Opt Lasers Eng 90:196–208

    Google Scholar 

  20. Katz J, Lindell Y (2008) Introduction to modern cryptography: principles and protocols cryptography and network security

  21. Khan JS, Ahmad J, Ahmed SS, Siddiqa HA, Abbasi SF, Kayhan SK (2019) Dna key based visual chaotic image encryption. J Intell Fuzzy Syst 37(2):2549–2561

    Google Scholar 

  22. Lima JB, Novaes L F G (2014) Image encryption based on the fractional fourier transform over finite fields. Signal Process 94:521–530

    Google Scholar 

  23. Lima JxB, da Silva ES, de Souza RMC (2015) A finite field cosine transform-based image processing scheme for color image encryption. In: 2015 IEEE Global Conference on Signal and Information Processing (globalSIP). IEEE, pp 1071–1075

  24. Liu H, Wang X et al (2012) Image encryption using dna complementary rule and chaotic maps. Appl Soft Comput 12(5):1457–1466

    Google Scholar 

  25. Malini S, Moni R S (2015) Image denoising using multiresolution singular value decomposition transform. Procedia Comput Sci 46:1708–1715

    Google Scholar 

  26. Mao Y, Chen G, Lian S (2004) A novel fast image encryption scheme based on 3d chaotic baker maps. Int J Bifurcat Chaos 14(10):3613–3624

    MathSciNet  MATH  Google Scholar 

  27. Martin K, Lukac R, Plataniotis KN (2005) Efficient encryption of wavelet-based coded color images. Pattern Recogn 38(7):1111–1115

    MATH  Google Scholar 

  28. Musanna F, Kumar S (2019) A novel fractional order chaos-based image encryption using fisher yates algorithm and 3-d cat map. Multimed Tools Appl 78 (11):14867–14895

    Google Scholar 

  29. Patidar V, Pareek N K, Purohit G, Sud KK (2011) A robust and secure chaotic standard map based pseudorandom permutation-substitution scheme for image encryption. Opt Commun 284(19):4331–4339

    Google Scholar 

  30. Phatak S C, Rao SS (1995) Logistic map a possible random-number generator. Phys Rev E 51(4):3670

    Google Scholar 

  31. Rukhin A, Soto J, Nechvatal J, Smid M, Barker E (2001) A statistical test suite for random and pseudorandom number generators for cryptographic applications. Technical report, Booz-Allen and Hamilton Inc Mclean Va

  32. Scharinger J (1998) Fast encryption of image data using chaotic kolmogorov flows. J Electron Imaging 7(2):318–326

    Google Scholar 

  33. Sharma K, Bala S, Bansal H, Shrivastava G (2017) Introduction to the special issue on secure solutions for network in scalable computing. Scalable Comput Pract Exper 18(3):iii–iv

  34. Shaw AK, Majumder S (2016) Multiresolution svd and pixel-wise masking based image watermarking. In: 2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC). IEEE, pp 193–196

  35. Shrivastava G, Gia NN, Bouhlel MS, Sharma K (2018) Special issue on advance research in model driven security, privacy and forensic of smart devices preface

  36. Shrivastava G, Kumar P, Gupta BB, Bala S, Dey N (2018) Handbook of research on network forensics and analysis techniques. IGI Global

  37. Singh LD, Singh KM (2018) Visually meaningful multi-image encryption scheme. Arab J Sci Eng 43(12):7397–7407

    Google Scholar 

  38. Szudzik M (2006) An elegant pairing function. In: Wolfram Research (ed.) Special NKS 2006 Wolfram Science Conference, pp 1–12

  39. Wen W, Zhang Y, Fang Y, Fang Z (2018) Image salient regions encryption for generating visually meaningful ciphertext image. Neural Comput Appl 29(3):653–663

    Google Scholar 

  40. Wu Y, Noonan J P, Agaian S (2011) NPCR and UACI randomness tests for image encryption. Cyber journals: Multidisciplinary journals in science and technology Journal of Selected Areas in Telecommunications (JSAT), pp 31–38

  41. Yang Y-G, Pan Q-X, Sun S-J, Xu P (2015) Novel image encryption based on quantum walks. Sci Rep 5(1):1–9

    Google Scholar 

  42. Yang Y-G, Zhang Y-C, Chen X-B, Zhou Y-H, Shi W-M (2018) Eliminating the texture features in visually meaningful cipher images. Inf Sci 429:102–119

    MathSciNet  Google Scholar 

  43. Ye G, Wong K-W (2012) An efficient chaotic image encryption algorithm based on a generalized arnold map. Nonlinear Dyn 69(4):2079–2087

    MathSciNet  Google Scholar 

  44. Yeganeh H, Wang Z (2013) Objective quality assessment of tone-mapped images. IEEE Trans Image Process 22(2):657–667

    MathSciNet  MATH  Google Scholar 

  45. Zeghid M, Machhout M, Khriji L, Baganne A, Tourki R (2007) A modified aes based algorithm for image encryption. Int J Comput Sci Eng 1(1):70–75

    Google Scholar 

Download references

Acknowledgments

One of the authors, Farhan Musanna, is grateful to the Ministry of Human Resource Development, India and the Indian Institute of Technology, Roorkee for being the funding agency of this work. The grant number is MHR-01-23-200-428.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjeev Kumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

We give a theoretical justification of the invertibility of the function defined in Section 2.4.

Case 1::

When n1max(n1, n2). Let \(z=\sigma (n_{1},n_{2})=n_{1}+{n_{2}^{2}}\). Define \(t={n_{2}^{2}}\) so that z = t + n1. Since

$$t={n_{2}^{2}}\implies n_{2}=\pm \sqrt{t},~\text{but since}~n_{2}>0\implies n_{2}=\sqrt{t}$$
$$\text{Thus,}~ n_{2}=h(t)=\sqrt{t},~\text{now since}~ h^{\prime}(t)=\frac{1}{2\sqrt{t}}>0,~\forall t>0 ~\text{hence one-one and}$$

clearly onto ⇒ h is invertible with h− 1(n2) = t2.

$$\text{Now}, ~t\leq t+n_{1}< t+2n_{2}+1 \implies {n_{2}^{2}}\leq z< (n_{2}+1)^{2}$$
$$\text{Thus,}~ h^{-1}(n_{2})\leq z< h^{-}(n_{2}+1)$$
$$ n_{2}\leq h(z) <n_{2}+1$$
$$n_{2}\leq \sqrt{z}<n_{2}+1$$

Now since n2 and n2 + 1 are two consecutive numbers, thus we obtain that

$$ \begin{array}{@{}rcl@{}} n_{2}=\lfloor(\sqrt{z})\rfloor \end{array} $$
(35)
$$ \begin{array}{@{}rcl@{}} n_{1}=z-(n_{2})^{2} \end{array} $$
(36)
Case 2::

When n1 = max(n1, n2). Let \(z={n_{1}^{2}}+n_{1}+n_{2}\), define \(t={n_{1}^{2}}+n_{1}\) so that z = t + n2. Since,

$$t={n_{1}^{2}}+n_{1}\implies n_{1}=\frac{-1\pm\sqrt{(1+4t)}}{2},~\text{but since}~n_{1}\geq 0$$
$$\implies n_{1}=\frac{-1+\sqrt{(1+4t)}}{2}$$
$$\text{Thus,}~ n_{1}=\frac{-1+\sqrt{(1+4t)}}{2}~\text{, proceeding as above we see that~} h~\text{is invertible}$$
$$\text{Now},~t\leq t+n_{2}<t+2n_{1}+1\implies {n_{1}^{2}}+n_{1}\leq z<(n_{1}+1)^{2}+(n_{1}+1) $$
$$\text{Thus,}~ h^{-1}(n_{1})\leq z< h^{-1}(n_{1}+1)$$
$$ n_{1}\leq h(z) <n_{1}+1$$
$$n_{1}\leq \frac{-1+\sqrt{(1+4z)}}{2}<n_{1}+1$$
$$ \begin{array}{@{}rcl@{}} n_{1}=\Bigg\lfloor\frac{-1+\sqrt{(1+4z)}}{2}\Bigg\rfloor \end{array} $$
(37)
$$ \begin{array}{@{}rcl@{}} n_{2}=z-{n_{1}^{2}}-n_{1} \end{array} $$
(38)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Musanna, F., Dangwal, D. & Kumar, S. A novel chaos-based approach in conjunction with MR-SVD and pairing function for generating visually meaningful cipher images. Multimed Tools Appl 79, 25115–25142 (2020). https://doi.org/10.1007/s11042-020-09034-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09034-x

Keywords

Navigation