Skip to main content
Log in

Introducing real-time image encryption technology using key vault, various transforms, and phase masks

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

Abstract

In today’s world achieving safe and efficient image transmission is a major issue. The image is encrypted by using some encryption technique and each algorithm has its strengths and weaknesses no single encryption mechanism can get the maximum security with minimum execution time. This paper proposes that it is possible to develop a Real-Time encryption algorithm system that uses multiple encryption algorithms in one system and provides adequate means of efficiency with security. In this system, we introduce a key vault that holds information of multiple encryption algorithms with a unique key. For every input image, the system selects a random algorithm for encryption from the key vault, encrypts the image with the selected encryption algorithm, and attached the key generated in the key vault to the encrypted image. At the receiver end first, we find out the key to identify the algorithm by which the image is encrypted, then get encryption algorithm details and perform decryption accordingly. We can increase the rate of its difficulty to improve its resistance against attacks by adding more and more encryption algorithms. It is quite impossible to break the proposed encryption technique as it randomly changes the encryption algorithm for each input image in randomly at run-time. The proposed cryptosystem can resist most of the existing attacks, as proved by using performance metrics and results. The aim is to provide a more secure image encryption system with reasonable computational complexity. This project has been implemented, key vault in ASP.NET and transforms and masks in MATLAB (2019a). Experiments confirm the feasibility and efficiency of the proposed real-time encryption system.

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

Similar content being viewed by others

References

  1. Akhavan A, Mahmodi H, Akhshani A (2006) A new image encryption algorithm based on one-dimensional polynomial chaotic maps. In: Computer and Information Sciences–ISCIS 2006: 21th International Symposium, Istanbul, Turkey, November 1-3, 2006. Proceedings 21. Springer Berlin Heidelberg, pp 963–971

  2. Akhshani A, Akhavan A, Lim S-C, Hassan Z (2012) An image encryption scheme based on quantum logistic map. Commun Nonlinear Sci Numer Simul 17(12):4653–4661. https://doi.org/10.1016/j.cnsns.2012.05.033

    Article  MathSciNet  MATH  Google Scholar 

  3. Azzaz MS, Tanougast C, Sadoudi S, Dandache A (2013) Robust chaotic key stream generator for real-time images encryption. J Real-Time Image Proc 8(3):297–306

    Article  MATH  Google Scholar 

  4. Bansal, M, Kumar, M, Sachdeva, M, Mittal, A, (2021) Transfer learning for image classification using VGG19: Caltech-101 image data set. J Ambient Intell Human Comput, pp.1-12

  5. Bansal M, Kumar M, Kumar M (2021) 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multimed Tools Appl 80(12):18839–18857

    Article  Google Scholar 

  6. Behnia S, Akhavan A, Akhshani A, Samsudin A (2013) Image encryption based on the jacobian elliptic maps. J Syst Softw 86(9):2429–2438. https://doi.org/10.1016/j.jss.2013.04.088

    Article  Google Scholar 

  7. Boriga R, Dascalescu AC, Priescu I (2014) A new hyperchaotic map and its application in an image encryption scheme. Signal Process Image Commun 29(8):887–901. https://doi.org/10.1016/j.image.2014.04.001

    Article  Google Scholar 

  8. Carnicer A, Montes-Usategui M, Arcos S, Juvells I (2005) Vulnerability to chosen-cyphertext attacks of optical encryption schemes based on double random phase keys. Opt Lett 30(13):1644–1646

    Article  Google Scholar 

  9. Dhopavkar, TA, Nayak, SK, Roy, S, (2022) IETD: a novel image encryption technique using Tinkerbell map and duffing map for IoT applications. Multimedi Tools Appl1–40

  10. Diwakar, M, Kumar, M, (2015) CT image denoising based on complex wavelet transform using local adaptive thresholding and bilateral filtering. In proceedings of the third international symposium on women in computing and informatics (pp. 297-302)

  11. Diwakar M, Kumar M (2018) A review on CT image noise and its denoising. Biomed Signal Process Control 42:73–88

    Article  Google Scholar 

  12. Diwakar M, Kumar P (2019) Wavelet packet based CT image denoising using bilateral method and Bayes shrinkage rule. In handbook of multimedia information security: techniques and applications (pp. 501-511). Springer, Cham

    Google Scholar 

  13. Diwakar M, Kumar P (2020) Blind noise estimation-based CT image denoising in tetrolet domain. Int J Inf Comput Secur 12(2–3):234–252

    Google Scholar 

  14. Diwakar M, Singh P (2020) CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomed Signal Process Control 57:101754

    Article  Google Scholar 

  15. Diwakar M, Verma A, Lamba S, Gupta H (2019) Inter-and intra-scale dependencies-based CT image denoising in curvelet domain. In soft computing: theories and applications (pp. 343-350). Springer, Singapore

    Google Scholar 

  16. Diwakar M, Kumar P, Singh AK (2020) CT image denoising using NLM and its method noise thresholding. Multimed Tools Appl 79(21):14449–14464

    Article  Google Scholar 

  17. Dong C (2014) Color image encryption using one-time keys and coupled chaotic systems. Signal Process Image Commun 29(5):628–640. https://doi.org/10.1016/j.image.2013.09.006

    Article  Google Scholar 

  18. Duan X, Liu J, Zhang E (2019) Efficient image encryption and compression based on a VAE generative model. J Real-Time Image Proc 16(3):765–773

    Article  Google Scholar 

  19. Elrefaey A, Sarhan A, El-Shennawy NM (2021) Parallel approaches to improve the speed of chaotic-maps-based encryption using GPU. J Real-Time Image Proc:1–10

  20. Girija R, Singh H (2018) A cryptosystem based on deterministic phase masks and fractional Fourier transform deploying singular value decomposition. Opt Quantum Electron 50(5):1–24

    Article  Google Scholar 

  21. Girija R, Singh H (2018) Symmetric cryptosystem based on chaos structured phase masks and equal modulus decomposition using fractional Fourier transform. 3D Res 9(3):1–20

    Article  Google Scholar 

  22. Girija R, Singh H (2019) Triple-level cryptosystem using deterministic masks and modified gerchberg-saxton iterative algorithm in fractional Hartley domain by positioning singular value decomposition. Optik 187:238–257

    Article  Google Scholar 

  23. Goodman, JW, (2005) Introduction to Fourier optics. Roberts and Company Publishers

  24. Gopinathan U, Monaghan DS, Naughton TJ, Sheridan JT (2006) A known-plaintext heuristic attack on the Fourier plane encryption algorithm. Opt Express 14(8):3181–3186

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Kadir A, Hamdulla A, Guo W-Q (2014) Color image encryption using skew tent map and hyper chaotic system of 6th-order cnn. 125(5):1671–1675

  27. Kaur M, Singh S, Kaur M (2021) Computational image encryption techniques: a comprehensive review. Math Probl Eng 2021:117

    MATH  Google Scholar 

  28. Kaur A, Kumar M, Jindal MK (2022) Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Ecol Inf 68:101549

    Article  Google Scholar 

  29. Khurana M, Singh H (2018) Asymmetric optical image triple masking encryption based on Gyrator and Fresnel transforms to remove silhouette problem. 3D Res 9(3):1–17

    Article  Google Scholar 

  30. Khurana M, Singh H (2019) A spiral-phase rear mounted triple masking for secure optical image encryption based on gyrator transform. Recent Patents Comput Sci 12(2):80–94

    Article  Google Scholar 

  31. Kumar M, Jindal SR (2019) Fusion of RGB and HSV colour space for foggy image quality enhancement. Multimed Tools Appl 78(8):9791–9799

    Article  Google Scholar 

  32. Kumar M, Chhabra P, Garg NK (2018) An efficient content-based image retrieval system using BayesNet and K-NN. Multimed Tools Appl 77(16):21557–21570

    Article  Google Scholar 

  33. Li F, Wu H, Zhou G, Wei W (2019) Robust real-time image encryption with aperiodic chaotic map and random-cycling bit shift. J Real-Time Image Proc 16(3):775–790

    Article  Google Scholar 

  34. Lu Q, Zhu C, Deng X (2020) An efficient image encryption scheme based on the LSS chaotic map and single S-box. IEEE Access 8:25664–25678

    Article  Google Scholar 

  35. Maan P, Singh H (2018) Non-linear cryptosystem for image encryption using radial Hilbert mask in fractional Fourier transform domain. 3D Res 9(4):1–12

    Article  Google Scholar 

  36. 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. https://doi.org/10.1142/S021812740401151X

    Article  MathSciNet  MATH  Google Scholar 

  37. Peng X, Wei H, Zhang P (2006) Chosen-plaintext attack on lensless double-random phase encoding in the Fresnel domain. Opt Lett 31(22):3261–3263

    Article  Google Scholar 

  38. Peng X, Zhang P, Wei H, Yu B (2006) Known-plaintext attack on optical encryption based on double random phase keys. Opt Lett 31(8):1044–1046

    Article  Google Scholar 

  39. Refregier P, Javidi B (1995) Optical image encryption based on input plane and Fourier plane random encoding. Opt Lett 20(7):767–769

    Article  Google Scholar 

  40. Sam IS, Devaraj P, Bhuvaneswaran RS, Devaraj ISSP, Bhuvaneswaran RS (2010) A novel image cipher based on mixed transformed logistic maps. Multimed Tools Appl 56(2):315–330. https://doi.org/10.1007/s11042-010-0652-6

    Article  Google Scholar 

  41. Sankpal, PR, Vijaya, PA, (2014) Image encryption using chaotic maps: a survey. In 2014 fifth international conference on signal and image processing (pp. 102-107). IEEE

  42. Shah AA, Parah SA, Rashid M, Elhoseny M (2020) Efficient image encryption scheme based on generalized logistic map for real time image processing. J Real-Time Image Proc 17(6):2139–2151

    Article  Google Scholar 

  43. Shaheed K, Mao A, Qureshi I, Kumar M, Hussain S, Ullah I, Zhang X (2022) DS-CNN: a pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Syst Appl 191:116288

    Article  Google Scholar 

  44. Singh H (2016) Devil′ s vortex Fresnel lens phase masks on an asymmetric cryptosystem based on phase-truncation in gyrator wavelet transform domain. Opt Lasers Eng 81:125–139

    Article  Google Scholar 

  45. Singh H (2017) Nonlinear optical double image encryption using random-optical vortex in fractional Hartley transform domain. Opt Appl 47(4)

  46. Singh H (2018) Hybrid structured phase mask in frequency plane for optical double image encryption in gyrator transform domain. J Mod Opt 65(18):2065–2078

    Article  MathSciNet  Google Scholar 

  47. Singh H (2018) Watermarking image encryption using deterministic phase mask and singular value decomposition in fractional Mellin transform domain. IET Image Process 12(11):1994–2001

    Article  Google Scholar 

  48. Singh H, Yadav AK, Vashisth S, Singh K (2014) A cryptosystem for watermarking based on fractional Fourier transform using a random phase mask in the input plane and structured phase mask in the frequency plane. Asian J Phys 23(4):597–612

    Google Scholar 

  49. Singh H, Yadav AK, Vashisth S, Singh K (2015) Optical image encryption using devil’s vortex toroidal lens in the Fresnel transform domain. Int J Opt 2015. https://doi.org/10.1155/2015/926135

  50. Srinivasu, PN, Norwawi, N, Amiripalli, SS, Deepalakshmi, P (n.d.) Secured compression for 2D medical images through the manifold and fuzzy trapezoidal correlation function. Gazi Univ J Sci

  51. Talhaoui, MZ, Wang X, Midoun MA (2020) "Fast image encryption algorithm with high security level using the Bülban chaotic map." J Real-Time Image Process : 1–14

  52. Tang Z, Xu S, Ye D, Wang J, Zhang X, Yu C (2019) Real-time reversible data hiding with shifting block histogram of pixel differences in encrypted image. J Real-Time Image Proc 16:709–724. https://doi.org/10.1007/s11554-018-0838-0

    Article  Google Scholar 

  53. Wan W, Wang J, Zhang Y, Li J, Yu H, Sun J (2022) A comprehensive survey on robust image watermarking. Neurocomputing. 488:226–247

    Article  Google Scholar 

  54. Wang X, Hou Y, Wang S, Li R (2018) A new image encryption algorithm based on CML and DNA sequence. IEEE Access 6:62272–62285

    Article  Google Scholar 

  55. Xuejing K, Zihui G (2020) A new color image encryption scheme based on DNA encoding and spatiotemporal chaotic system. Signal Process Image Commun 80:115670

    Article  Google Scholar 

  56. Yan SF, Shu Tang L, Zong Wang L (2007) Image encryption using high-dimension chaotic system. Chin Phys 16(12):3616–3623. https://doi.org/10.1088/1009-1963/16/12/011

    Article  Google Scholar 

  57. Yap W-S, Phan RC-W, Yau W-C, Heng S-H (2015) Cryptanalysis of a new image alternate encryption algorithm based on chaotic map. Nonlinear Dyn 80(3):14831491

    Article  MathSciNet  MATH  Google Scholar 

  58. Zhang C, Liao M, He W, Peng X (2013) Ciphertext-only attack on a joint transform correlator encryption system. Opt Express 21(23):28523–28530

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Poonam Yadav.

Ethics declarations

Conflict of interest

To the best of our knowledge, this work does not have any financial and/or non-financial conflict of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, P., Singh, H. & Khanna, K. Introducing real-time image encryption technology using key vault, various transforms, and phase masks. Multimed Tools Appl 82, 39099–39117 (2023). https://doi.org/10.1007/s11042-023-14715-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14715-4

Keywords

Navigation