Skip to main content
SpringerLink
Log in

A visually meaningful image encryption algorithm based on adaptive 2D compressive sensing and chaotic system

  • 1187: Recent Advances in Multimedia Information Security: Cryptography and Steganography
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A novel visually meaningful image encryption algorithm is proposed based on adaptive 2D compressive sensing and chaotic system. The plain image is first compressed and encrypted simultaneously by adaptive 2D compressive sensing to obtain the pre-encrypted compressed image. In this process, 3D cat map is used to generate the measurement matrix and the scrambling sequence. Then, the pre-encrypted compressed image is embedded into the host image by dynamic LSB method based on 2K correction so as to get cipher images with higher visual quality. A four-dimensional discrete chaotic system is used for region scrambling in the embedding process in order to further improve the security of the algorithm. In the simulation tests, the plain image with the maximum size 2048 × 2048 can be compressed and embedded into a host image of size 512 × 512. The embedding ratio is better than the existing algorithms. Most importantly, our algorithm is tens or even hundreds of times more efficient than other algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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. Armijo-Correa JO, Murguía JS, Mejía-Carlos M et al (2020) An improved visually meaningful encrypted image scheme. Opt Laser Technol 127:106165

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  3. Calderbank AR, Daubechies I, Sweldens W et al (1998) Wavelet transforms that map integers to integers. Appl Comput Harmon Anal 5(3):332–369

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  5. Chai X, Zheng X, Gan Z et al (2018) An image encryption algorithm based on chaotic system and compressive sensing. Signal Process 148:124–144

    Article  Google Scholar 

  6. Chai X, Fu X, Gan Z et al (2019) A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Process 155:44–62

    Article  Google Scholar 

  7. Chai X, Wu H, Gan Z et al (2020) An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding. Opt Lasers Eng 124:105837

    Article  Google Scholar 

  8. Chai X, Wu H, Gan Z et al (2020) Hiding cipher-images generated by 2-D compressive sensing with a multi-embedding strategy. Signal Process 171:107525

    Article  Google Scholar 

  9. Chai X, Wu H, Gan Z et al (2021) An efficient approach for encrypting double color images into a visually meaningful cipher image using 2D compressive sensing. Inf Sci 556:305–340

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen E, Min L, Chen G (2017) Discrete chaotic systems with one-line equilibria and their application to image encryption. Int J Bifurc Chaos 27(03):1750046

    Article  MathSciNet  MATH  Google Scholar 

  11. Data Encryption Standard (1977) Federal information processing standards publication (FIPS PUB) no. 46, National Bureau of Standards, Washington, DC

  12. Deng J, Zhao S, Wang Y et al (2017) Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform. Multimed Tools Appl 76(7):10097–10117

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  14. Eftekhari A, Babaie-Zadeh M, Moghaddam HA (2011) Two-dimensional random projection. Signal Process 91(7):1589–1603

    Article  MATH  Google Scholar 

  15. Gao Z, Xiong C, Ding L et al (2013) Image representation using block compressive sensing for compression applications. J Vis Commun Image Represent 24(7):885–894

    Article  Google Scholar 

  16. Hua Z, Zhou Y (2016) Image encryption using 2D Logistic-adjusted-Sine map. Inf Sci 339:237–253

    Article  Google Scholar 

  17. Hua Z, Zhang K, Li Y et al (2021) Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing. Signal Process 183:107998

    Article  Google Scholar 

  18. Huo D, Zhu Z, Wei L et al (2021) A visually secure image encryption scheme based on 2D compressive sensing and integer wavelet transform embedding. Opt Commun 492:126976

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Mallat SG, Zhang ZF (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415

    Article  MATH  Google Scholar 

  21. Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for over complete sparse decomposition based on smoothed l0 norm. IEEE Trans Signal Process 57(1):289–301

    Article  MathSciNet  MATH  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(33):25115–25142

    Article  Google Scholar 

  23. National Institute of Standards and Technology (2001) Advanced encryption standard (AES)

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

    Article  Google Scholar 

  25. Sun S (2016) A novel edge-based image steganography with 2k correction and Huffman encoding. Inf Process Lett 116(2):93–99

    Article  MathSciNet  MATH  Google Scholar 

  26. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666

    Article  MathSciNet  MATH  Google Scholar 

  27. Tuncer T, Dogan S, Tadeusiewicz R et al (2019) Improved reference image encryption methods based on 2K correction in the integer Wavelet domain. Int J Appl Math Comput Sci 29(4):817–829

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Wu Y, Noonan JP, Agaian S (2011) NPCR and UACI randomness tests for image encryption. J Sel Areas Telecommun (JSAT) 1(2):31–38

    Google Scholar 

  31. Xu GB, Jiang DH (2021) Novel methods to construct nonlocal sets of orthogonal product states in an arbitrary bipartite high-dimensional system. Quantum Inf Process 20:128

  32. Yang YG, Tian J, Lei H et al (2016) Novel quantum image encryption using one-dimensional quantum cellular automata. Inf Sci 345:257–270

    Article  Google Scholar 

  33. Yang YG, Zhang YC, Chen XB et al (2018) Eliminating the texture features in visually meaningful cipher images. Inf Sci 429:102–119

    Article  MathSciNet  Google Scholar 

  34. Yang YG, Guan BW, Li J et al (2019) Image compression-encryption scheme based on fractional order hyper-chaotic systems combined with 2D compressed sensing and DNA encoding. Opt Laser Technol 119:105661

    Article  Google Scholar 

  35. Yang YG, Zou L, Zhou YH et al (2020) Visually meaningful encryption for color images by using Qi hyper-chaotic system and singular value decomposition in YCbCr color space. Optik 213:164422

    Article  Google Scholar 

  36. Yang YG, Wang BP, Yang YL et al (2021) Visually meaningful image encryption based on universal embedding model. Inf Sci 562:304–324

    Article  MathSciNet  Google Scholar 

  37. Yang YG, Wang BP, Yang YL et al (2021) Dual embedding model: a new framework for visually meaningful image encryption. Multimed Tools Appl 80:9055–9074

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. Ye GD, Pan C, Dong YX, Jiao KX, Huang XL (2021) A novel multi-image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition. Trans Emerg Telecommun Technol 32(2):4071

    Google Scholar 

  40. Zhang Y, Zhang LY, Zhou J et al (2016) A review of compressive sensing in information security field. IEEE Access 4:2507–2519

    Article  Google Scholar 

  41. Zhou RG, Wu Q, Zhang MQ et al (2013) Quantum image encryption and decryption algorithms based on quantum image geometric transformations. Int J Theor Phys 52(6):1802–1817

    Article  MathSciNet  Google Scholar 

  42. Zhou N, Li H, Wang D et al (2015) Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform. Opt Commun 343:10–21

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Zhu L, Song H, Zhang X et al (2020) A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding. Signal Process 175:107629

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62071015, 62171264).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Yu-Guang Yang, Bao-Pu Wang, Yong-Li Yang, Yi-Hua Zhou & Wei-Min Shi

  2. Beijing Key Laboratory of Trusted Computing, Beijing, 100124, China

    Yu-Guang Yang

  3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Xin Liao

Authors
  1. Yu-Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bao-Pu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong-Li Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi-Hua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei-Min Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Guang Yang.

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

Yang, YG., Wang, BP., Yang, YL. et al. A visually meaningful image encryption algorithm based on adaptive 2D compressive sensing and chaotic system. Multimed Tools Appl 82, 22033–22062 (2023). https://doi.org/10.1007/s11042-021-11656-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11656-8

Keywords

Search

Navigation