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Lossless image compression-encryption algorithm based on BP neural network and chaotic system

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Abstract

In this paper, a fractional-order memristive band-pass filter (BPF) chaotic circuit is constructed base on BPF chaotic circuit and fractional definition. The attractor and fractal characteristics are analyzed through phase diagrams and time domain response diagrams. In addition, randomness of the chaotic pseudo-random sequences is tested through NIST SP800–22 and correlation of sequence. According to the fractional-order chaotic system and Back-Propagation (BP) neural network, a lossless image compression-encryption algorithm is proposed. In this algorithm, the original image is compressed through BP neural network, and then the compressed image is encrypted by using Zigzag algorithm and xor operation. Numerical simulation results show that the proposed algorithm not only can effectively compression-encryption image, but also have the great security performances, which provides theoretical guide for the application of this algorithm in information safety, and secret communication field.

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References

  1. Ye G, Wong KW (2013) An image encryption scheme based on time-delay and hyperchaotic system [J]. Nonlinear Dynamics 71(1–2):259–267

    Article  MathSciNet  Google Scholar 

  2. Wang XY, Zhang YQ, Bao XM (2015) A novel chaotic image encryption scheme using DNA sequence operations [J]. Opt Lasers Eng 73:53–61

    Article  Google Scholar 

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

  4. Tomar RRS, Jain K (2016) Lossless image compression using differential pulse code modulation and its application[C]// fifth international conference on communication systems & network technologies. IEEE

  5. Alshehri, Ali S. Neural network technique for image compression [J]. IET Image Processing, 2015.

  6. Egmont-Petersen M, Ridder DD, Handels H (2002) Image processing with neural networks - a review. Pattern Recogn 35:2279C2301 [J]. Pattern Recogn 35(10):2279–2301

    Article  MATH  Google Scholar 

  7. Dony RD, Haykin S (1995) Neural network approaches to image compression [J]. Proc IEEE 83(2):288–303

    Article  Google Scholar 

  8. Daugman JG (1988) Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression [J]. IEEE Trans acoust speech & Signal Process 36(7):1169–1179

    Article  MATH  Google Scholar 

  9. Kouamo S, Tangha C (2013) Image compression with artificial neural networks [J]. Advances in Intelligent Systems & Computing 189:515–524

    Article  Google Scholar 

  10. Costa S, Fiori S (2001) Image compression using principal component neural networks [J]. Image & Vision Computing Journal 19(9):649–668

    Article  Google Scholar 

  11. Hui Fang L, Mo L. (2010) A New Method of Image Compression Based on Quantum Neural Network[C]// International Conference of Information Science & Management Engineering. IEEE Computer Society

  12. Yeo W K, Yap D F W, Lim K C, et al. A feedforward neural network compression with near to lossless image quality and lossy compression ratio[C]// research & development. .

  13. Singh Y S, Devi B P, Singh K M. 2013 Image compression using multilayer feed forward artificial neural network with conjugate gradient[C]// Information & Communication Technologies

  14. Yan S, Zhong X.2013 Application of BP neural network with Chebyshev mapping in image compression[C]// third international conference on instrumentation

  15. Sahami S, Shayesteh MG (2012) Bi-level image compression technique using neural networks [J]. IET Image Process 6(5):496–506

    Article  MathSciNet  Google Scholar 

  16. Alshehri SA (2016) Neural network technique for image compression [J]. IET Image Process 10(3):222–226

    Article  MathSciNet  Google Scholar 

  17. Al-Allaf O N A. Fast Back propagation neural network algorithm for reducing convergence time of BPNN image compression[C]// international conference on Information Technology & Multimedia. 2012.

    Google Scholar 

  18. Amerijckx C, Legat JD, Thissen P et al (1998) Image compression by self-organized Kohonen map[J]. IEEE Trans Neural Netw 9(3):503–507

    Article  Google Scholar 

  19. Xu HK, Yang WS, Fang JW et al (2012) A rapid method for image compression based on wavelet transform and SOFM neural network [J]. Appl Mech Mater 135-136:126–131

    Article  Google Scholar 

  20. Upadhyay P, Chhabra JK (2015) Modified self organizing feature map neural network (MSOFM NN) based gray image segmentation [J]. Procedia Computer Science 54:671–675

    Article  Google Scholar 

  21. Denk T, Parhi K K, Cherkassky V.1993 Combining neural networks and the wavelet transform for image compression[C]// IEEE international conference on acoustics. .

  22. Khashman A, Dimililer K. Image compression using neural networks and haar wavelet [J].2008 Wseas Transactions on Signal Processing

  23. Hussain AJ, Al-Jumeily D, Radi N et al (2015) Hybrid neural network predictive-wavelet image compression system[J]. Neurocomputing 151:975–984

    Article  Google Scholar 

  24. 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.

  25. Wu X, Zhu B, Hu Y, et al. A novel colour image encryption scheme using rectangular transform-enhanced chaotic tent maps [J]. IEEE Access, 2017, PP (99):1–1.

  26. Tong XJ, Chen P, Miao Z (2016) A joint image lossless compression and encryption method based on chaotic map [J]. Multimed Tools Appl 76(12):1–26

    Google Scholar 

  27. Masmoudi A, Puech W (2014) Lossless chaos-based crypto-compression scheme for image protection [J]. IET Image Process 8(12):671–686

    Article  Google Scholar 

  28. Kumar S R, Binod K, Kumar S D, 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:S1319157817304123-.

  29. Brindha M, Gounden NA (2016) A chaos based image encryption and lossless compression algorithm using hash table and Chinese remainder theorem [J]. Appl Soft Comput 40:379–390

    Article  Google Scholar 

  30. Zhu H, Cheng Z, Zhang X (2013) A novel image encryption–compression scheme using hyper-chaos and Chinese remainder theorem [J]. Signal Process Image Commun 28(6):670–680

    Article  Google Scholar 

  31. Zhou N, Zhang A, Wu J et al (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing [J]. Optik - International Journal for Light and Electron Optics 125(18):5075–5080

    Article  Google Scholar 

  32. Ye Z, Xu B, Zhou N (2017) A novel image compression–encryption hybrid algorithm based on the analysis sparse representation [J]. Opt Commun 392:223–233

    Article  Google Scholar 

  33. Bao B, Ning W, Quan X et al (2017) A simple third-order Memristive band pass filter chaotic circuit [J]. IEEE Transactions on Circuits & Systems II Express Briefs 64(8):977–981

    Article  Google Scholar 

  34. Zhang L, Sun K, He S et al (2017) Solution and dynamics of a fractional-order 5-D hyperchaotic system with four wings [J]. European Physical Journal Plus 132(1):31

    Article  Google Scholar 

  35. Xu Y, Sun K, He S et al (2016) Dynamics of a fractional-order simplified unified system based on the Adomian decomposition method [J]. European Physical Journal Plus 131(6):1–12

    Article  Google Scholar 

  36. He S, Sun K, Wang H. 2016 Dynamics of the fractional-order Lorenz system based on Adomian decomposition method and its DSP implementation[J]. IEEE/CAA Journal of Automatica Sinica, , PP(99):1–6.

  37. Ruan J, Sun K, Mou J et al (2018) Fractional-order simplest memristor-based chaotic circuit with new derivative [J]. European Physical Journal Plus 133(1):3

    Article  Google Scholar 

  38. Rukhin AL, Soto J, Nechvatal JR et al (2010) SP 800-22 rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications [J]. Appl Phys Lett 22(7):1645–1179

    Google Scholar 

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

    Article  Google Scholar 

  40. Chai X, Zheng X, Gan Z et al (2018) An image encryption algorithm based on chaotic system and compressive sensing [J]. Signal Process 148:S0165168418300549

    Article  Google Scholar 

  41. Liang Y, Liu G, Zhou N et al (2015) Image encryption combining multiple generating sequences controlled fractional DCT with dependent scrambling and diffusion[J]. J Mod Opt 62(4):251–264

    Article  Google Scholar 

  42. Wu X, Zhu B, Hu Y, et al. 2017 A novel colour image encryption scheme using rectangular transform-enhanced chaotic tent maps [J]. IEEE Access, PP(99):1–1.

Download references

Acknowledgments

This work was 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).

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Authors and Affiliations

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

    Feifei Yang, Jun Mou & Ran Chu

  2. School of Physics and Electronics, Central South University, Changsha, 410083, China

    Kehui Sun

Authors
  1. Feifei Yang
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  2. Jun Mou
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  3. Kehui Sun
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  4. Ran Chu
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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. Kehui Sun designed and improved the algorithm. Ran Chu improved the algorithm. All authors reviewed the manuscript.

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Correspondence to Jun Mou.

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Yang, F., Mou, J., Sun, K. et al. Lossless image compression-encryption algorithm based on BP neural network and chaotic system. Multimed Tools Appl 79, 19963–19992 (2020). https://doi.org/10.1007/s11042-020-08821-w

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  • DOI: https://doi.org/10.1007/s11042-020-08821-w

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