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An efficient meaningful double-image encryption algorithm based on parallel compressive sensing and FRFT embedding

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Abstract

The transmission of images via the Internet has grown exponentially in the past few decades. However, the Internet considered as an insecure method of information transmission may cause serious privacy issues. To overcome such potential security issues, a novel visually meaningful double-image encryption (VMDIE) algorithm conjugating quantum cellular neural network (QCNN), compressive sensing (CS) and fractional Fourier transform (FRFT) is proposed in this paper. First, the wavelet coefficients of two plain images are scrambled by the Fisher-Yates confusion algorithm, and compressed by key-controlled partial Hadamard matrix. The final meaningful cipher image is generated by embedding the encrypted images into a same-scale host image via the FRFT-based embedding approach. Besides, the eigenvalues of plain images are utilized to generate secret key streams to improve the ability of proposed VMDIE algorithm to withstand various plaintext attacks. Afterward, the plaintext eigenvalues are hidden into the alpha channel of meaningful cipher image under control of keys to relieve unnecessary storage space and transmission cost. Ultimately, simulation results and security analyses indicate that the proposed VMDIE algorithm is effective and can withstand multiple attacks.

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Data availability

The image datasets generated and analyzed during the current study are available from the corresponding author upon the reasonable request.

References

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

    MathSciNet  MATH  Google Scholar 

  2. Baraniuk R (2007) Compressive sensing. IEEE Signal Process Mag 24:118–121

    Google Scholar 

  3. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52:489–509

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  5. 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 

  6. Chai X, Wu H, Gan Z, Zhang Y, Chen Y, Nixon K (2020) An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt Laser Eng 124:105837

    Google Scholar 

  7. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306

    MathSciNet  MATH  Google Scholar 

  8. Elhoseny H, Faragallah O, Ahmed H, Kazemian H, El-sayed H, El-Samie F (2016) The effect of fractional Fourier transform angle in encryption quality for digital images. Optik 127:315–319

    Google Scholar 

  9. Fan H, Zhou K, Zhang E, Wen W, Li M (2020) Subdata image encryption scheme based on compressive sensing and vector quantization. Neural Comput Applic 32:12771–12787

    Google Scholar 

  10. Gan Z, Chai X, Zhang J, Zhang Y, Chen Y (2020) An effective image compression-encryption scheme based on compressive sensing (CS) and game of life (GOL). Neural Comput Applic 32:14113–14141

    Google Scholar 

  11. Gong L, Qiu K, Deng C, Zhou N (2019) An image compression and encryption algorithm based on chaotic system and compressive sensing. Opt Laser Technol 115:257–267

    Google Scholar 

  12. Henon M (1976) A two-dimensional mapping with a strange attractor. Commun Math Phys 50(1):69–77

    MathSciNet  MATH  Google Scholar 

  13. Hu G, Xiao D, Wang Y, Xiang T (2017) An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image Represent 44:116–127

    Google Scholar 

  14. Hu G, Xiao D, Wang Y, Xiang T, Zhou Q (2017) Securing image information using double random phase encoding and parallel compressive sensing with updated sampling processes. Opt Laser Eng 98:123–133

    Google Scholar 

  15. Hua Z, Zhou Y (2021) Exponential chaotic model for generating robust chaos. IEEE Trans Syst Man Cybern -Syst 51(6):3713–3724

    Google Scholar 

  16. Hua ZY, Zhang KY, Li YM, Zhou YC (2021) Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing. Signal Process 183:107998

    Google Scholar 

  17. Huang R, Rhee K, Uchida S (2014) A parallel image encryption method based on compressive sensing. Multimed Tools Appl 72:71–93

    Google Scholar 

  18. Khan J, Boulila W, Ahmad J, Rubaiee S, Rehman A, Alroobaea R, Buchanan W (2020) DNA and plaintext dependent chaotic visual selective image encryption. IEEE Access 8:159732–159744

    Google Scholar 

  19. Li J, Di X, Liu X, Chen X (2017) Image encryption based on quantum-CNN hyperchaos system and anamorphic fractional Fourier transform. In 10th International Congress on Image and Signal Processing. J Biomed Eng Inform:1–6

  20. Li M, Wang P, Yue Y, Liu Y (2021) Cryptanalysis of a secure image encryption scheme based on a novel 2D sine-cosine cross-chaotic map. J Real-Time Image Proc 18:2135–2149

    Google Scholar 

  21. Mondal B, Singh S, Kumar P (2019) A secure image encryption scheme based on cellular automata and chaotic skew tent map. J Inf Secur Appl 45:117–130

    Google Scholar 

  22. Musanna F, Dangwal D, Kumar S (2020) 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

    Google Scholar 

  23. 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 78:14867–14895

    Google Scholar 

  24. Naskar P, Bhattacharyya S, Nandy D, Chaudhuri A (2020) A robust image encryption scheme using chaotic tent map and cellular automata. Nonlinear Dyn. 100:2877–2898

    Google Scholar 

  25. Pak C, Huang L (2017) A new color image encryption using combination of the 1D chaotic map. Signal Process 138:129–137

    Google Scholar 

  26. Pan C, Ye G, Huang X, Zhou J (2019) Novel meaningful image encryption based on block compressive sensing. Secur Commun Netw:6572105

  27. Ping P, Fu J, Mao Y, Xu F, Gao J (2019) Meaningful encryption: generating visually meaningful encrypted images by compressive sensing and reversible color transformation. IEEE Access 7:170168–170184

    Google Scholar 

  28. Ping P, Xu F, Mao Y, Wang Z (2018) Designing permutation-substitution image encryption networks with Henon map. Neuro-computing. 283:53–63

    Google Scholar 

  29. Ponuma R, Amutha R (2017) Compressive sensing based image compression-encryption using novel 1D-chaotic map. Multimed Tools Appl 77:19209–19234

    Google Scholar 

  30. Rachlin, Y., Baron, D. The secrecy of compressed sensing measurements. in: Proceedings of the Allerton Conference on Communi-cation, Control and Computing, 813–817 (2008).

  31. Souyah A, Faraoun K (2016) Fast and efficient randomized encryption scheme for digital images based on quadtree decomposition and reversible memory cellular automata. Nonlinear Dyn 84(2):715–732

    MathSciNet  Google Scholar 

  32. Wang H, Xiao D, Li M, Xiang Y, Li X (2019) A visually secure image encryption scheme based on parallel compressive sensing. Signal Process 155:218–232

    Google Scholar 

  33. Wang X, Guan N (2020) A novel chaotic image encryption algorithm based on extended zigzag confusion and RNA operation. Opt Laser Technol 131:106366

    Google Scholar 

  34. Wang X, Li Z (2019) A color image encryption algorithm based on Hopfield chaotic neural network. Opt Laser Eng 115:107–118

    Google Scholar 

  35. Wang X, Su Y (2020) Color image encryption based on chaotic compressed sensing and two-dimensional fractional Fourier transform. Sci Rep 10:18556

    Google Scholar 

  36. Wen W, Hong Y, Fang Y, Li M, Li M (2020) A visually secure image encryption scheme based on semi-tensor product compressed sensing. Signal Process 173:107580

    Google Scholar 

  37. Wen W, Wei K, Zhang Y, Fang Y, Li M (2020) Colour light field image encryption based on DNA sequences and chaotic systems. Nonlinear Dyn. 99:1587–1600

    Google Scholar 

  38. Yang F, Mou J, Cao Y, Chu R (2020) An image encryption algorithm based on BP neural network and hyperchaotic system. China Commun 17(5):21–28

    Google Scholar 

  39. Yang Y, Zou L, Zhou Y, Shi W (2020) Visually meaningful encryption for color images by using qi hyperchaotic system and singular value decomposition in YCbCr color space. Optik 213:164422

    Google Scholar 

  40. Ye G, Pan C, Dong Y, Jiao K, Huang X (2021) A novel multi-image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition. Trans Emerg Telecommun Technol 32(2):e4071

    Google Scholar 

  41. Ye G, Pan C, Dong Y, Shi Y, Huang X (2020) Image encryption and hiding algorithm based on compressive sensing and random numbers insertion. Signal Process 172:107563

    Google Scholar 

  42. Ye G, Pan C, Huang X, Zhao Z, He J (2018) A chaotic image encryption algorithm based on information entropy. Int J Bifurcation Chaos 28(1):1850010

    MathSciNet  MATH  Google Scholar 

  43. Zhang L, Wong K, Zhang Y, Zhou J (2016) Bi-level protected compressive sampling. IEEE T Multimedia 18(9):1720–1732

    Google Scholar 

  44. Zhao H, Xie S, Zhang J, Wu T (2020) Fast image encryption algorithm based on improved Henon map. Application Research of Computers 37(12):3726–3730

    Google Scholar 

  45. Zhao H, Ye H, Wang R (2016) The construction of measurement matrices based on block weighing matrix in compressed sensing. Signal Process 123:64–74

    Google Scholar 

  46. Zhou K, Xu M, Luo J, Fan H, Li M (2019) Cryptanalyzing an image encryption based on a modified Henon map using hybrid chaotic shift transform. Digit Signal Process 93:115–127

    Google Scholar 

  47. Zhou N, Pan S, Cheng S, Zhou Z (2016) Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133

    Google Scholar 

  48. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression-encryption algorithm based on compressive sensing. Optik 125:5075–5080

    Google Scholar 

  49. Zhu L, Song H, Zhang X, Yan M, Zhang L, Yan T (2019) A novel image encryption scheme based on nonuniform sampling in block compressive sensing. IEEE Access 7:22161–22174

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China [Grant No.61701043, 41874140], the Shaanxi Province Science and Technology Program [Grant No.2020JM-220, 2020JQ-351], the Fundamental Research Funds for the Central Universities of China [Grant No.300102240205], the Natural Science Foundation of Fujian Province [Grant No.2020 J05169] and the Natural Science Foundation of Heilongjiang Province [Grant No.F2018022].

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Donghua Jiang & Lidong Liu

  2. School of Information Engineering, Chang’an University, Xi’an, China

    Lidong Liu

  3. School of Electrical and Control Engineering, Chang’an University, Xi’an, 710064, China

    Liya Zhu

  4. School of Information Science and Technology, Dalian Maritime University, Da’lian, 116026, China

    Xingyuan Wang

  5. School of Physics and Information Engineering, Minnan Normal University, Zhang’zhou, 363000, China

    Yingpin Chen

  6. Physics and Electronic Engineering School, Harbin Normal University, Harbin, 150025, China

    Xianwei Rong

Authors
  1. Donghua Jiang
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  2. Lidong Liu
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  3. Liya Zhu
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  4. Xingyuan Wang
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  5. Yingpin Chen
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  6. Xianwei Rong
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Correspondence to Lidong Liu.

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Jiang, D., Liu, L., Zhu, L. et al. An efficient meaningful double-image encryption algorithm based on parallel compressive sensing and FRFT embedding. Multimed Tools Appl 82, 27337–27363 (2023). https://doi.org/10.1007/s11042-023-14601-z

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