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Image Compression and Encryption Algorithm Based on Hyper-chaotic Map

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Abstract

In this paper, aiming at defects which are low security properties, high costs of storage and transmission for exiting image encryption and compression algorithms. An algorithm which combined image compression and encryption based on hyper-chaotic map is proposed. In this algorithm, the original image is compressed by compression sensing (CS), and then the compressed image is encrypted through improved Arnold matrix transformation algorithm, Modular operation algorithm and combined the 3D hyper-chaotic map. The experimental results and theoretical analyses show that the proposed algorithm has superior safety performance and compression characteristics, which may reduce the costs of data transmission and improve the encryption efficiency. What’s more, it provides the theoretical guidance and experimental basis for digital image encryption in practical application.

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References

  1. Wang X, Liu C, Xu D et al (2016) Image encryption scheme using chaos and simulated annealing algorithm[J]. Nonlinear Dynamics 84(3):1417–1429

    Article  MathSciNet  Google Scholar 

  2. Yi S, Zhou Y (2017) Binary-Block Embedding for Reversible Data Hiding in Encrypted Images [J]. Signal Process 133:40–51

    Article  Google Scholar 

  3. Guesmi R, Farah MAB, Kachouri A et al (2016) Hash key-based image encryption using crossover operator and chaos[J]. Multimed Tools Appl 75(8):4753–4769

    Article  Google Scholar 

  4. Matthews R (1989) A rotor device for periodic and random-key encryption[J]. Cryptologia 13(3):266–272

    Article  Google Scholar 

  5. Li C, Luo G, Qin K et al (2017) An image encryption scheme based on chaotic tent map[J]. Nonlinear Dyn 87(1):127–133

    Article  Google Scholar 

  6. Çavuşoğlu Ü, Kaçar S, Pehlivan I et al (2017) Secure image encryption algorithm design using a novel chaos based S-Box[J]. Chaos, Solitons Fractals 95:92–101

    Article  Google Scholar 

  7. Zhu H, Zhang X, Yu H et al (2017) An image encryption algorithm based on compound homogeneous hyper-chaotic system[J]. Nonlinear Dyn 89(1):1–19

    Article  Google Scholar 

  8. Luo Y, Zhou R, Liu J et al. (2018) A parallel image encryption algorithm based on the piecewise linear chaotic map and hyper-chaotic map[J]. Nonlinear Dynamics, (5):1-17

  9. Zhu C, Sun K (2018) Cryptanalyzing and Improving a Novel Color Image Encryption Algorithm Using RT-Enhanced Chaotic Tent Maps [J]. IEEE Access, PP (99):1-1

  10. Wang XY, Gu SX, Zhang YQ (2015) Novel image encryption algorithm based on cycle shift and chaotic system[J]. Opt Lasers Eng 68:126–134

    Article  Google Scholar 

  11. Luo Y, Du M, Liu J (2015) A symmetrical image encryption scheme in wavelet and time domain[J]. Commun Nonlinear Sci Numer Simul 20(2):447–460

    Article  Google Scholar 

  12. Zhu C, Xu S, Hu Y et al (2015) Breaking a novel image encryption scheme based on Brownian motion and PWLCM chaotic system[J]. Nonlinear Dyn 79(2):1511–1518

    Article  Google Scholar 

  13. Zhang Q, Guo L, Wei X (2010) Image encryption using DNA addition combining with chaotic maps [J]. Math Comput Model 52(11):2028–2035

    Article  MathSciNet  Google Scholar 

  14. Wang XY, Zhang YQ, Zhao YY (2015) A novel image encryption scheme based on 2-D logistic map and DNA sequence operations[J]. Nonlinear Dyn 82(3):1269–1280

    Article  MathSciNet  Google Scholar 

  15. Hu T, Liu Y, Gong LH et al (2017) Chaotic image cryptosystem using DNA deletion and DNA insertion[J]. Signal Process 134(C):234–243

    Article  Google Scholar 

  16. Zhang L M, Sun K H, Liu W H, et al. (2017) A novel color image encryption scheme using fractional-order hyperchaotic system and DNA sequence operations [J], 26 (10): 98-106

  17. Donoho DL (2006) Compressed sensing[J]. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  18. Xiaowei MA (2015) Improvement of OMP Image Reconstruction Algorithm Based on Compressed Sensing [J]. Electronic Science & Technology

  19. Needell D, Tropp JA (2009) CoSaMP: Iterative Signal Recovery From Incomplete and Inaccurate Samples[J]. Appl Comput Harmon Anal 26(3):301–321

    Article  MathSciNet  Google Scholar 

  20. Lin NI (2001) Lossless Compression of Multispectral Remote Sensing Images Based on Improved SP[J]. Acta Electronica Sinica

  21. Figueiredo MAT, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[C]// IEEE Journal of Selected Topics in Signal Processing. IEEE Journal of Selected Topics in Signal Processing, 586 – 597

  22. Krzakala F, Mézard M, Sausset F et al (2011) Statistical physics-based reconstruction in compressed sensing [J]. Phys Rev X 2(2):1952–1954

    Google Scholar 

  23. Efron B, Hastie T, Johnstone I et al. (2004) Rejoinder to "Least angle regression" by Efron et al [J]. Annals of Statistics, 32

  24. Poli L, Oliveri G, Rocca P et al (2013) Bayesian Compressive Sensing Approaches for the Reconstruction of Two-Dimensional Sparse Scatterers Under TE Illuminations[J]. IEEE Trans Geosci Remote Sens 51(5):2920–2936

    Article  Google Scholar 

  25. Rachlin Y, Baron D (2008) The secrecy of compressed sensing measurements [C]// Communication, Control, and Computing. Allerton Conference on IEEE 2008:813–817

    Google Scholar 

  26. Mayiami MR, Seyfe B, Bafghi HG (2013) Perfect secrecy via compressed sensing[C]// Communication and Information Theory. IEEE, 1-5

  27. Liu X, Cao Y, Lu P et al (2013) Optical image encryption technique based on compressed sensing and Arnold transformation[J]. Optik - International Journal for Light and Electron Optics 124(24):6590–6593

  28. Kim B, Lee BG, Situ G et al (2015) Compressive sensing based robust multispectral double-image encryption[J]. Appl Opt 54(7):1782–1793

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Chai X, Zheng X, Gan Z et al. (2018) An image encryption algorithm based on chaotic system and compressive sensing[J]. Signal Processing, 148

  31. Huang R, Rhee KH, Uchida S (2014) A parallel image encryption method based on compressive sensing[J]. Multimed Tools Appl 72(1):71–93

    Article  Google Scholar 

  32. George SN, Augustine N, Pattathil DP (2015) Audio security through compressive sampling and cellular automata[J]. Multimed Tools Appl 74(23):10393–10417

    Article  Google Scholar 

  33. George SN, Pattathil DP (2014) A secure LFSR based random measurement matrix for compressive sensing[J]. Sensing and Imaging 15(1):85–240

  34. Zhang Z, Jung TP, Makeig S et al (2013) Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning[J]. IEEE Trans Biomed Eng 60(2):300–309

    Article  Google Scholar 

  35. Chen L, Sun X, Jiang H et al. (2014) A High-Performance Control Method of Constant V/f-Controlled Induction Motor Drives for Electric Vehicles[J]. Mathematical Problems in Engineering, 2014, (2014-1-21), 2014(2):317-348

  36. Liu W, Sun K, He Y et al (2017) Color Image Encryption Using Three-Dimensional Sine ICMIC Modulation Map and DNA Sequence Operations[J]. Int J Bifurcation Chaos 27(11):1750171

    Article  Google Scholar 

  37. Zhou N, Zhang A, Zheng F et al (2014) Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing[J]. Opt Laser Technol 62(10):152–160

    Article  Google Scholar 

  38. Singh RK, Kumar B, Shaw DK et al. (2018) Level by level image compression-encryption algorithm based on Quantum chaos map[J]. Journal of King Saud University-Computer and Information Sciences

  39. Guo L, Chen J, Li J (2017) Chaos-Based color image encryption and compression scheme using DNA complementary rule and Chinese remainder theorem[C]// International Computer Conference on Wavelet Active Media Technology and Information Processing. IEEE

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Acknowledgments

This investigate is supported by the Basic Scientific Research Projects of Colleges and Universities of Liaoning Province (Grant Nos. 2017 J045); Provincial Natural Science Foundation of Liaoning (Grant Nos. 20170540060); Scientific Research Projects in General of Liaoning Province (Grant Nos. L2015043), Doctoral Research Startup Fund Guidance Program of Liaoning Province (Grant Nos. 201601280).

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Authors and Affiliations

  1. School of Information Science and Engineering, Dalian polytechnic University, Dalian, 116034, China

    Jun Mou, Feifei Yang, Ran Chu & Yinghong Cao

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  1. Jun Mou
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  2. Feifei Yang
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  3. Ran Chu
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  4. Yinghong Cao
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Contributions

Feifei Yang designed and carried out experiments, data analyzed and manuscript wrote. Jun Mou made the theoretical guidance for this paper. Ran Chu and Yinghong Cao made a technical support for this paper. Every author went over this manuscript carefully.

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Correspondence to Jun Mou.

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Mou, J., Yang, F., Chu, R. et al. Image Compression and Encryption Algorithm Based on Hyper-chaotic Map. Mobile Netw Appl 26, 1849–1861 (2021). https://doi.org/10.1007/s11036-019-01293-9

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