Cloud-decryption-assisted image compression and encryption based on compressed sensing | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Cloud-decryption-assisted image compression and encryption based on compressed sensing

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

Abstract

In this paper, we propose a new image compression and encryption scheme (ICES) based on compressed sensing (CS) and double random phase encoding (DRPE), as well as a joint decryption on the cloud and user side. In the encryption, the plain image is firstly decomposed into approximate component and detail components by discrete wavelet transform (DWT), then the approximate component is scrambled, and detail components are compressed using a measurement matrix constructed by the Logistic-tent system. Secondly, the scrambled approximate component and compressed detail components are combined into a complex matrix, and then it is subjected to DRPE to get the amplitude and phase matrices. Subsequently, the resulting matrices are performed random pixel scrambling and diffusion to obtain the final cipher image. During the decryption, the cipher image is first partially decrypted on the cloud, and then fully decrypted to recover the original image on the user side, and we can judge whether the cloud has cheated us by comparing the contour similarity between the approximate component and the detail component returned by the cloud to the user, which not only significantly shortens the decryption time, but also effectively prevents malicious deception of the cloud. Experimental results and performance analyses demonstrate its effectiveness and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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

Similar content being viewed by others

References

  1. Anand A, Raj A, Kohli R, Bibhu V (2016) Proposed symmetric key cryptography algorithm for data security. 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH), Noida, pp. 159-162

  2. Anwar S, Meghana S (2019) A pixel permutation based image encryption technique using chaotic map. Multimed Tools Appl 78(19):27569–27590

    Google Scholar 

  3. Arab A, Rostami MJ, Ghavami B (2019) An image encryption method based on chaos system and AES algorithm. J Supercomput 75:6663–6682

    Google Scholar 

  4. Ashwini K, Amutha R (2018) Fast and secured cloud assisted recovery scheme for compressively sensed signals using new chaotic system. Multimed Tools Appl 77(31):31581–31606

    Google Scholar 

  5. Candès EJ, Romberg J (2006) Quantitative robust uncertainty principles and optimally sparse decompositions. Found Comput Math 6(2):227–254

    MathSciNet  MATH  Google Scholar 

  6. Chai XL, Zheng XY, Gan ZH, Han DJ, Chen YR (2018) An image encryption algorithm based on chaotic system and compressive sensing. Signal Process 148:124–144

    Google Scholar 

  7. Chai XL, Wu HY, Gan ZH, Zhang YS, Chen YR Nixon KW (2020) An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt lasers Eng 124: UNSP 105837

  8. Chai XL, Bi JQ, Gan ZH, Liu XX, Zhang YS, Chen YR (2020) Color image compression and encryption scheme based on compressive sensing and double random encryption strategy. Signal Process 176:107684

    Google Scholar 

  9. Chai XL, Zheng XY, Gan ZH, Chen YR (2020) Exploiting plaintext-related mechanism for secure color image encryption. Neural Comput Applic 32(12):8065–8088

    Google Scholar 

  10. Chai XL, Zhi XC, Gan ZH, Zhang YS, Chen YR, Fu JY (2021) Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption. Signal Process 183:108041

    Google Scholar 

  11. Chen TH, Zhang M, Wu J, Yuen C, Tong Y (2016) Image encryption and compression based on kronecker compressed sensing and elementary cellular automata scrambling. Opt Laser Technol 84:118–133

    Google Scholar 

  12. Chen JX, Zhang Y, Qi L, Fu C, Xu LS (2018) Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression. Opt Laser Technol 99:238–248

    Google Scholar 

  13. Chen JX, Zhu ZL, Zhang LB, Zhang YS, Yang BQ (2018) Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption. Signal Process 142:340–353

    Google Scholar 

  14. Chen MJ, He XT, Gong LH, Chen RL (2019) Image compression and encryption scheme with hyper-chaotic system and mean error control. J Mod Opt 66(13):1416–1424

    MathSciNet  Google Scholar 

  15. Chen JX, Chen L, Zhang LY, Zhu ZL (2019) Medical image cipher using hierarchical diffusion and non-sequential encryption. Nonlinear Dynam 96(1):301–322

    Google Scholar 

  16. Darwish SM (2019) A modified image selective encryption-compression technique based on 3D chaotic maps and arithmetic coding. Multimed Tools Appl 78(14):19229–19252

    Google Scholar 

  17. Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306

    MathSciNet  MATH  Google Scholar 

  18. Gan ZH, Chai XL, Han DJ, Chen YR (2019) A chaotic image encryption algorithm based on 3-D bit-plane permutation. Neural Comput Applic 31(11):7111–7130

    Google Scholar 

  19. Ghazvini M, Mirzadi M, Parvar N (2020) A modified method for image encryption based on chaotic map and genetic algorithm. Multimed Tools Appl 79(37–38):26927–26950

    Google Scholar 

  20. Gong LH, Deng CZ, Pan SM, Zhou NR (2018) Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform. Opt Laser Technol 103:48–58

    Google Scholar 

  21. Gong LH, Qiu KD, Deng CZ, Zhou NR (2019) An image compression and encryption algorithm based on chaotic system and compressive sensing. Opt Laser Technol 115:257–267

    Google Scholar 

  22. Gong LH, Qiu KD, Deng CZ, Zhou NR (2019) An optical image compression and encryption scheme based on compressive sensing and RSA algorithm. Opt Lasers Eng 121:169–180

    Google Scholar 

  23. Hu GQ, Xiao D, Xiang T, Bai S, Zhang YS (2017) A compressive sensing based privacy preserving outsourcing of image storage and identity authentication service in cloud. Inf Sci 387:132–145

    MATH  Google Scholar 

  24. Hua ZY, Xu BX, Jin F, Huang HJ (2019) Image encryption using Josephus problem and filtering diffusion. IEEE Access 7:8660–8674

    Google Scholar 

  25. Hua ZY, Zhu ZH, Yi S, Zhang Z, Huang HJ (2021) Cross-plane colour image encryption using a two-dimensional logistic tent modular map. Inf Sci 546:1063–1083

    MathSciNet  Google Scholar 

  26. Jha DP, Kohli R, Gupta A (2016) Proposed encryption algorithm for data security using matrix properties. 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH), Noida, pp. 86-90

  27. Khedr WI (2020) A new efficient and configurable image encryption structure for secure transmission. Multimed Tools Appl 79(23–24):16797–16821

    Google Scholar 

  28. Li LL, Xie YY, Liu YZ (2019) Exploiting optical chaos for color image encryption and secure resource sharing in cloud. IEEE Photonics J 11(3):1503112

    Google Scholar 

  29. Liang YR, Xiao ZY (2020) Image encryption algorithm based on compressive sensing and fractional DCT via polynomial interpolation. Int J Autom Comput 17(2):292–304

    Google Scholar 

  30. Liu L, Wang LF, Shi YQ, Chang CC (2019) Separable data-hiding scheme for encrypted image to protect privacy of user in cloud. Symmetry-Basel 11(1):82

    Google Scholar 

  31. Luo YL, Lin J, Liu JX, Wei DQ, Cao LC, Zhou RL, Cao Y, Ding XM (2019) A robust image encryption algorithm based on Chua’s circuit and compressive sensing. Signal Process 161:227–247

    Google Scholar 

  32. Ma S, Zhang Y, Yang ZG, Hu JH, Lei X (2019) A new plaintext-related image encryption scheme based on chaotic sequence. IEEE Access 7:30344–30360

    Google Scholar 

  33. Pan C, Ye GD, Huang XL, Zhou JW (2019) Novel meaningful image encryption based on block compressive sensing. Secur Commun Netw 2019:6572105–6572112

    Google Scholar 

  34. Patel U, Dadhania P (2019) Multilevel data encryption using AES and RSA for image and textual information data. 2019 innovations in power and advanced computing technologies (I-PACT), Vellore

  35. Ponuma R, Amutha R (2019) Encryption of image data using compressive sensing and chaotic system. Multimed Tools Appl 78(9):11857–11881

    MATH  Google Scholar 

  36. Som S, Mitra A, Palit S, Chaudhuri BB (2018) A selective bitplane image encryption scheme using chaotic maps. Multimed Tools Appl 78(8):10373–10400

    Google Scholar 

  37. Song YJ, Zhu ZL, Zhang W, Guo L, Yang X, Yu H (2019) Joint image compression-encryption scheme using entropy coding and compressive sensing. Nonlinear Dynam 95(3):2235–2261

    MATH  Google Scholar 

  38. Telem ANK, Fotsin HB, Kengne J (2021) Image encryption algorithm based on dynamic DNA coding operations and 3D chaotic systems. Multimed Tools Appl 80:19011–19041. https://doi.org/10.1007/s11042-021-10549-0

    Article  Google Scholar 

  39. USC-SIPI (1977) The USC-SIPI Image Database. Available: http://sipi.usc.edu/database/database.php?volume=misc. Accessed 20 Mar 2020

  40. Wang XY, Gao S (2020) Image encryption algorithm for synchronously updating Boolean networks based on matrix semi-tensor product theory. Inf Sci 507:16–36

    MathSciNet  MATH  Google Scholar 

  41. Xian YJ, Wang XY, Yan XP, Li Q, Wang XY (2020) Image encryption based on chaotic sub-block scrambling and chaotic digit selection diffusion. Opt Lasers Eng 134:106202

    Google Scholar 

  42. Xie YQ, Yu JY, Guo SY, Ding Q, Wang E (2019) Image encryption scheme with compressed sensing based on new three-dimensional chaotic system. Entropy-Switz 21(9):819

    MathSciNet  Google Scholar 

  43. Xu QY, Sun KH, He SB, Zhu CX (2020) An effective image encryption algorithm based on compressive sensing and 2D-SLIM. Opt Lasers Eng 134:106178

    Google Scholar 

  44. Yang FF, Mou J, Liu J, Ma CG, Yan HZ (2020) Characteristic analysis of the fractional-order hyperchaotic complex system and its image encryption application. Signal Process 169:107373

    Google Scholar 

  45. Yao SY, Chen LF, Zhong Y (2019) An encryption system for color image based on compressive sensing. Opt Laser Technol 120:105703

    Google Scholar 

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

    Google Scholar 

  47. Yu SS, Zhou NR, Gong LH, Nie Z (2020) Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system. Opt Lasers Eng 124:105816

    Google Scholar 

  48. Zhang R, Xiao D (2020) A secure image permutation-substitution framework based on chaos and compressive sensing. Int J Distrib Sens Netw 16(3) 1550147720912949

  49. Zhang YS, Huang H, Xiang Y, Zhang LY, He X (2017) Harnessing the hybrid cloud for secure big image data service. IEEE Internet Things 4(5):1380–1388

    Google Scholar 

  50. Zhang YS, He Q, Xiang Y, Zhang LY, Liu B, Chen JX, Xie YY (2018) Low-cost and confidentiality-preserving data acquisition for internet of multimedia things. IEEE Internet Things 5(5):3442–3451

    Google Scholar 

  51. Zhang YS, Xiang Y, Zhang LY, Yang LX, Zhou JT (2019) Efficiently and securely outsourcing compressed sensing reconstruction to a cloud. Inf Sci 496:150–160

    Google Scholar 

  52. Zhang YS, He Q, Chen G, Zhang XP, Xiang Y (2019) A low-overhead, confidentiality-assured, and authenticated data acquisition framework for IoT. IEEE Trans Ind Inform 16(12):7566–7578

    Google Scholar 

  53. Zhang H, Wang XQ, Sun YJ, Wang XY (2020) A novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automata. Signal Process-Image 84:115829

    Google Scholar 

  54. Zhang YS, Wang P, Fang LM, He X, Han H, Chen B (2020) Secure transmission of compressed sampling data using edge clouds. IEEE Trans Ind Inform 16(10):6641–6651

    Google Scholar 

  55. Zhang Z, Bi HB, Kong XX, Li N, Lu D (2020) Adaptive compressed sensing of color images based on salient region detection. Multimed Tools Appl 79(21–22):14777–14791

    Google Scholar 

  56. Zhang Y, Chen AG, Tang YJ, Dang JY, Wang GP (2020) Plaintext-related image encryption algorithm based on perceptron-like network. Inf Sci 526:180–202

    MathSciNet  Google Scholar 

  57. Zhang YS, Wang P, Huang H, Zhu YW, Xiao D, Xiang Y (2021) Privacy-assured FogCS: chaotic compressive sensing for secure industrial big image data processing in fog computing. IEEE Trans Ind Inform 17(5):3401–3411

    Google Scholar 

  58. Zhou NR, Jiang H, Gong LH, Xie XW (2018) Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging. Opt Lasers Eng 110:72–79

    Google Scholar 

  59. Zhou KL, Fan JJ, Fan HJ, Li M (2020) Secure image encryption scheme using double random-phase encoding and compressed sensing. Opt Laser Technol 121:105769

    Google Scholar 

Download references

Acknowledgments

All the authors are deeply grateful to the editors for smooth and fast handling of the manuscript. The authors would also like to thank the anonymous referees for their valuable suggestions to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Grant No. 61802111, 61872125, 61871175), Science and Technology Foundation of Henan Province of China (Grant No. 182102210027, 182102410051), China Postdoctoral Science Foundation (Grant No. 2018 T110723), Key Scientific Research Projects for Colleges and Universities of Henan Province (Grant No. 19A413001), Natural Science Foundation of Henan Province (Grant No. 182300410164), Graduate Education Innovation and Quality Improvement Project of Henan University (Grant No. SYL18020105), Henan Higher Education Teaching Reform Research and Practice Project (Graduate Education) (Grant No. 2019SJGLX080Y), and the Key Science and Technology Project of Henan Province (Grant No. 201300210400, 212102210094).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Henan University, Kaifeng, 475004, China

    Jiangyu Fu, Xiuli Chai & Yang Lu

  2. School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Institute of Intelligent Network System, Henan University, Kaifeng, 475004, China

    Zhihua Gan

  3. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China

    Xiuli Chai & Yang Lu

Authors
  1. Jiangyu Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhihua Gan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiuli Chai
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yang Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Zhihua Gan or Yang Lu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Fu, J., Gan, Z., Chai, X. et al. Cloud-decryption-assisted image compression and encryption based on compressed sensing. Multimed Tools Appl 81, 17401–17436 (2022). https://doi.org/10.1007/s11042-022-12607-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12607-7

Keywords