Skip to main content
SpringerLink
Log in

Double image compression-encryption algorithm based on fractional order hyper chaotic system and DNA approach

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

Abstract

In order to improve the efficiency and security of image compression-encryption algorithms, we propose a double image compression-encryption scheme based on fractional hyper-chaotic system and DNA approach. Firstly, two images are processed by discrete cosine transform. Secondly, the spectrums of the two images are sorted by Z-scan, so that the two images can be compressed and mixed into a new image. Finally, the resulting image is encrypted by using DNA coding. Different from traditional image encryption algorithms, the proposed algorithm provides a variety of DNA coding and operation modes. Chaotic sequences are used to control the coding and operation mode in order to improve the complexity of the encryption process. Fractional order and initial values of fractional order hyper-chaotic system are used as the key of the proposed algorithm, which greatly expands the complexity and the key space of the scheme. In the proposed scheme, by mixing two images and performing a compression-encryption operation on them simultaneously, the proposed algorithm can improve the complexity of encrypted images while providing good confusion. Experimental results and security analysis show that the proposed algorithm can effectively resist multiple attacks.

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

Similar content being viewed by others

References

  1. “Data Encryption Standard” (1977) Federal Information Processing Standards Publication (FIPS PUB) No. 46, National Bureau of Standards, Washington, DC

  2. Alfalou A, Brosseau C (2010) Exploiting root-mean-square time-frequency structure for multiple-image optical compression and encryption. Opt Lett 35(11):1914–1916

    Article  Google Scholar 

  3. Baptista MS (1998) Cryptography with chaos. Phys Lett A 240(1–2):50–54

    Article  MathSciNet  MATH  Google Scholar 

  4. Bose R, Pathak S (2006) A novel compression and encryption scheme using variable model arithmetic coding and coupled chaotic system. IEEE Trans Circuits Syst I Reg Papers 53(4):848–857

    Article  MathSciNet  MATH  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

    Article  Google Scholar 

  6. Chai XL, Fu XL, Gan ZH, Lu Y, Chen YR (2019) A color image cryptosystem based on dynamic DNA encryption and chaos. Signal Process 155(1):44–62

    Article  Google Scholar 

  7. Chai X, Wu H, Gan Z, Zhang Y, Chen Y, Nixon KW (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, Zhang Y, Chen Y (2020) Hiding cipher-images generated by 2-D compressive sensing with a multi-embedding strategy. Signal Process 171:107525

    Article  Google Scholar 

  9. Chen GR, Ueta T (1999) Yet another chaotic attractor. Int J Bifurc Chaos 9:1465–1466

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen TH, Wu CS (2010) Compression-unimpaired batch-image encryption combining vector quantization and index compression. Inf Sci 180(9):1690–1701

    Article  Google Scholar 

  11. Chen JX, Zhang Y, Qi L (2018) Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression. Opt Laser Technol 99(2):238–248

    Article  Google Scholar 

  12. Deng J, Zhao S, Wang Y, Wang L, Wang H, Sha H (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. Dube S, Sharma K (2019) Hybrid approach to enhance contrast of image for forensic investigation using segmented histogram. Int J Adv Intell Paradigms 13(1–2):43–66

    Article  Google Scholar 

  15. Gong LH, Deng CZ, Pan SM (2018) Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform. Opt Laser Technol 103(1):48–58

    Article  Google Scholar 

  16. Grangetto M, Magli E, Olmo G (2006) Multimedia selective encryption by means of randomized arithmetic coding. IEEE Trans Multimedia 8(5):905–917

    Article  Google Scholar 

  17. Hermassi H, Rhouma R, Belghith S (2010) Joint compression and encryption using chaotically mutated Huffman trees. Commun Nonlinear Sci Numer Simul 15(10):2987–2999

    Article  MathSciNet  MATH  Google Scholar 

  18. Huffman DA (2006) A method for the construction of minimum-redundancy codes. Resonance 11(2):91–99

    Article  Google Scholar 

  19. Jakimoski G, Subbalakshmi KP (2008) Cryptanalysis of some multimedia encryption schemes. IEEE Trans Multimedia 10(3):330–338

    Article  Google Scholar 

  20. Kayalvizhi S, Malarvizhi S (2020) A novel encrypted compressive sensing of images based on fractional order hyper chaotic Chen system and DNA operations. Multimed Tools Appl 79(5–6):3957–3974

    Article  Google Scholar 

  21. Kim H, Wen JT, Villasenor J (2007) Secure arithmetic coding. IEEE Trans Signal Process 55(5):2263–2272

    Article  MathSciNet  MATH  Google Scholar 

  22. Landir M, Hamiche H, Kassim S (2019) A novel robust compression-encryption of images based on SPIHT coding and fractional-order discrete-time chaotic system. Opt Laser Technol 109(1):534–546

    Google Scholar 

  23. Langdon GG, Rissanen JJ (1979) Arithmetic coding. IBM J Res 23(2):149–162

    Article  MathSciNet  MATH  Google Scholar 

  24. Li PY, Lo KT (2018) A content-adaptive joint image compression and encryption scheme. IEEE Trans Multimed 20(8):1960–1972

    Article  Google Scholar 

  25. Liu X, Cao Y, Lu P, Lu X, Li Y (2013) Optical image encryption technique based on compressed sensing and Arnold transformation. Optik 124(24):6590–6593

    Article  Google Scholar 

  26. Lv XP, Liao XF, Yang B (2018) A novel scheme for simultaneous image compression and encryption based on wavelet packet transform and multi-chaotic systems. Multimed Tools Appl 77(21):28633–28663

    Article  Google Scholar 

  27. Nagaraj N, Vaidya PG, Bhat K (2009) Arithmetic coding as a non-linear dynamical system. Commun Nonlinear Sci Numer Simul 14(4):1013–1020

    Article  MathSciNet  MATH  Google Scholar 

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

  29. Ponuma R, Amutha R (2018) Compressive sensing based image compression-encryption using novel 1D-Chaotic map. Multimed Tools Appl 77(15):19209–19234

    Article  Google Scholar 

  30. 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 Exp 18(3):3–5

    Google Scholar 

  31. Shrivastava G, Pandey A, Sharma K (2013) Steganography and Its Technique: Technical Overview. Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Springer, New York

    Google Scholar 

  32. Shrivastava G, Nhu NG, Bouhlel MS, Sharma K (2017) Special issue on advance research in model driven security, privacy, and forensic of smart devices preface

  33. Shrivastava G, Kumar P, Gupta BB (2018) Handbook of research on network forensics and analysis techniques[M]

  34. Wang QZ, Wei MY, Chen XM (2018) Joint encryption and compression of 3D images based on tensor compressive sensing with non-autonomous 3D chaotic system. Multimed Tools Appl 77(2):1715–1734

    Article  Google Scholar 

  35. Watson JD, Crick FHC (1953) A structure for deoxyribose nucleic acid. Nature 171(4356):737–738

    Article  Google Scholar 

  36. Wen JT, Kim H, Villasenor J (2006) Binary arithmetic coding with key-based interval splitting. IEEE Signal Process Lett 13(2):69–72

    Article  Google Scholar 

  37. Yang YG, Guan BW, Li J, Li D, Zhou YH, Shi WM (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 

  38. Zhang YS, Xiao D, Liu H, Nan H (2014) GLS coding based security solution to JPEG with the structure of aggregated compression and encryption. Commun Nonlinear Sci Numer Simul 19(5):1366–1374

    Article  MATH  Google Scholar 

  39. Zhou J, Au OC (2008) Comments on “a novel compression and encryption scheme using variable model arithmetic coding and coupled chaotic system”. IEEE Trans Circuits Syst I Reg Papers 55(10):3368–2269

    Article  Google Scholar 

  40. Zhou J, Liang Z, Chen Y (2007) Security analysis of multimedia encryption schemes based on multiple Huffman table. IEEE Signal Process Lett 14(3):201–204

    Article  Google Scholar 

  41. Zhou J, Au OC, Wong PH (2009) Adaptive chosen-ciphertext attack on secure arithmetic coding. IEEE Trans Signal Process 57(5):1825–1838

    Article  MathSciNet  MATH  Google Scholar 

  42. Zhou N, Li H, Wang D, Pan S, Zhou Z (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 NR, Pan SM, Cheng S, Zhou ZH (2018) Image compression encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82(3):121–133

    Google Scholar 

  44. Zhu HG, Zhao C, Zhang XD (2013) A novel image encryption-compression scheme using hyper-chaos and Chinese remainder theorem. Signal Process Image Commun 28(9):670–680

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Beijing Municipal Science & Technology Commission (Project No. Z191100007119004), the Beijing Natural Science Foundation (Grant No. 4182006), and the Guangxi Key Laboratory of Cryptography and Information Security (Grant No. GCIS201810).

Author information

Authors and Affiliations

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

    Yu-Guang Yang, Bo-Wen Guan, Yi-Hua Zhou & Wei-Min Shi

  2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin, 541004, Guangxi, China

    Yu-Guang Yang

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

    Yu-Guang Yang

Authors
  1. Yu-Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo-Wen Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi-Hua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei-Min Shi
    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., Guan, BW., Zhou, YH. et al. Double image compression-encryption algorithm based on fractional order hyper chaotic system and DNA approach. Multimed Tools Appl 80, 691–710 (2021). https://doi.org/10.1007/s11042-020-09779-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09779-5

Keywords

Search

Navigation