Skip to main content
SpringerLink
Log in

Compressive sensing based image compression-encryption using Novel 1D-Chaotic map

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

Abstract

Compressive sensing based encryption achieves simultaneous compression-encryption by utilizing a low complex sampling process, which is computationally secure. In this paper, a new novel 1D–chaotic map is proposed that is used to construct an incoherence rotated chaotic measurement matrix. The chaotic property of the proposed map is experimentally analysed. The linear measurements obtained are confused and diffused using the chaotic sequence generated using the proposed map. The chaos based measurement matrix construction results in reduced data storage and bandwidth requirements. As it needs to store only the parameters required to generate the chaotic sequence. Also, the sensitivity of the chaos to the parameters makes the data transmission secure. The secret key used in the encryption process is dependent on both the input data and the parameter used to generate the chaotic map. Hence the proposed scheme can resist chosen plaintext attack. The key space of the proposed scheme is large enough to thwart statistical attacks. Experimental results and the security analysis verifies the security and effectiveness of the proposed compression-encryption scheme.

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

Similar content being viewed by others

References

  1. Banderia AS, Dobriban E, Mixon DG, Sawin WF (2012) Certifying the Restricted Isometry Property is Hard. IEEE Trans Inf Theory 59:3448–3450

    Article  MathSciNet  MATH  Google Scholar 

  2. Baraniuk R (2007) Compressive Sensing (lecture notes). IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  3. Belazi AAA, El-Latif BS (2016) A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process 128:155–170

    Article  Google Scholar 

  4. Cambareri V, Mangia M, Pareschi F, Rovatti R, Setti G (2015) On known-plaintext attacks to a compressed sensing-based encryption: a quantitative analysis. IEEE Trans Inf Forensics Security 10(10):2182–2195

    Article  Google Scholar 

  5. Cambareri V, Mangia M, Pareschi F, Rovatti R, Setti G (2015) Low-complexity multiclass encryption by compressed sensing. IEEE Trans Signal Process 63(9):2183–2195

    MathSciNet  Google Scholar 

  6. Candes EJ (2008) The restricted isometry property and its implications for compressed sensing. C R Math 346(9–10):589–592

    Article  MathSciNet  MATH  Google Scholar 

  7. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern 43(4):996–1002

    Article  Google Scholar 

  8. Deepak M, Ashwin V, Amutha R (2014) A new Multistage multiple image encryption using a combination of Chaotic Block Cipher and Iterative Fractional Fourier Transform. In First International Conference on Networks & Soft Computing (ICNSC2014):360–364

  9. Donoho DL (2006) Compressed Sensing. IEEE Trans Inf Theory 52:1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  10. Fay R (2016) Introducing the counter mode of operation to Compressed Sensing based encryption. Inform Process Lett 16(4):279–283

    Article  MathSciNet  MATH  Google Scholar 

  11. Gordon WB (1996) Period three trajectories of the logistic map. Math Mag 69(2):118–120

    Article  MathSciNet  MATH  Google Scholar 

  12. Hanis S, Amutha R (2017) Double image compression and encryption scheme using logistic mapped convolution and cellular automata. Multimed Tools Appl :1-16

  13. Hu G, Xiao D, Wang Y, Xiang T (2017) An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image R 44:116–127

    Article  Google Scholar 

  14. Hua Z, Zhou Y, Pun CM, Chen CP (2015) 2D Sine Logistic modulation map for image encryption. Inform Sciences 297:80–94

    Article  Google Scholar 

  15. Huang R, Sakurai K (2011) A robust and compression-combined digital image encryption method based on compressive sensing. In Proc Intelligent Information Hiding and Multimedia Signal Processing IIH-MSP, 105–108

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

    Article  Google Scholar 

  17. Jiang J, He X, Gao M, Wang X, Wu X (2015) Human action recognition via compressive-sensing-based dimensionality reduction. Optik-Int J Light Electron Optics 126(9):882–887

    Article  Google Scholar 

  18. Liao X, Shu C (2015) Reversible data hiding in encrypted images based on absolute mean difference of multiple neighboring pixels. J Vis Commun Image R 28:21–27

    Article  Google Scholar 

  19. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete Fourier transform. Multimed Tools Appl 76(20):20739–20753

    Article  Google Scholar 

  20. Liao X, Qin Z, Ding L (2017) Data embedding in digital images using critical functions. Signal Proces-Image 58:146–156

    Article  Google Scholar 

  21. Liu E, Temlyakov VN (2012) The orthogonal super greedy algorithm and application in compressed sensing. IEEE Trans Inf Theory 58:2040–2047

    Article  MathSciNet  MATH  Google Scholar 

  22. Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In Proc of IEEE Int Conf Virtual Systems and Multimedia (VSMM) :26–33

  23. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In Proc of IEEE Int Conf Patt Recog (ICPR) :898–901

  24. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. In Proc of Int Joint Conf Artif (IJCAI) :1617–1623

  25. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune Teller: Predicting Your Career Path. In Proc of AAAI :201-207

  26. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing Complex Activities by a Probabilistic Interval-Based Model. In Proc of AAAI 30:1266–1272

    Google Scholar 

  27. Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. In Proc of Int Joint Conf Artif (IJCAI) :2576–2582

  28. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 12(181):108–115

    Article  Google Scholar 

  29. Niyat AY, Moattar MH, Torshiz MN (2017) Color image encryption based on hybrid hyper-chaotic system and cellular automata. Opt Laser Eng 90:225–237

    Article  Google Scholar 

  30. Orsdemir A, Altun HO, Sharma G, Bocko MF (2008) On the security and robustness of encryption via compressed sensing. In IEEE Milit Commun C (MILCOM 2008) San Diego, CA :1-7

  31. Phamila AVY, Amutha R (2013) Low complexity energy efficient very low bit-rate image compression scheme for wireless sensor network. Inform Process Lett 113(18):672–676

    Article  MathSciNet  MATH  Google Scholar 

  32. Phamila AVY, Amutha R (2015) Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks. Electron Lett 51(11):824–826

    Article  Google Scholar 

  33. Preoţiuc-Pietro D, Liu Y, Hopkins D, Ungar L (2017) Beyond binary labels: political ideology prediction of Twitter users. In Proc of the 55th Annual Meeting of the Association for. Computational Linguistics 1:729–740

    Google Scholar 

  34. Rachlin Y, Baron D (2008) The secrecy of compressed sensing measurements. In Proc 46th Annual Allerton Conf. Comm. Control Comput :813-817

  35. Tong XJ, Wang Z, Zhang M, Liu Y (2013) A new algorithm of the combination of image compression and encryption technology based on cross chaotic map. Nonlinear Dynam 72(1–2):229–241

    Article  MathSciNet  Google Scholar 

  36. Wu X, Wang D, Kurths J, Kan H (2016) A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system. Inform Sciences 349:137–153

    Article  Google Scholar 

  37. Yao S, Wang T, Shen W, Shaoming P, Chong Y (2017) Research of incoherence rotated chaotic measurement matrix in compressed sensing. Multimed Tools Appl 76(17):17699–17717

    Article  Google Scholar 

  38. Yu L, Barbot JP, Zheng G, Sun H (2010) Compressive sensing with chaotic sequence. IEEE Signal Process Lett 17(8):731–734

    Article  Google Scholar 

  39. Yuan X, Wang X, Wang C, Weng J, Ren K (2016) Enabling secure and fast indexing for privacy-assured healthcare monitoring via compressive sensing. IEEE Trans Multimedia 18(10):2002–2014

    Article  Google Scholar 

  40. Yuen CH, Wong KW (2011) A chaos-based joint image compression and encryption scheme using DCT and SHA-1. Appl Soft Comput 11(8):5092–5098

    Article  Google Scholar 

  41. Zhang LY, Wong KW, Zhang Y, Zhou J (2016) Bi-level protected compressive sampling. IEEE Trans Multimedia 18(9):1720–1732

    Article  Google Scholar 

  42. Zhang W, Yu H, Zhao YL, Zhu ZL (2016) Image encryption based on three-dimensional bit matrix permutation. Signal Process 118:36–50

    Article  Google Scholar 

  43. Zhang Y, Zhang LY, Zhou J, Liu L, Chen F, He X (2016) A review of compressive sensing in information security field. IEEE Access 4:2507–2519

    Article  Google Scholar 

  44. Zhou NR, Zhang AD, Zheng F, Gong LH (2014) Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Opt Laser Technol 62:152–160

    Article  Google Scholar 

  45. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing. Optik-International Journal for Light and Electron Optics 125(18):5075–5080

    Article  Google Scholar 

  46. Zhou Y, Bao L, Chen CP (2014) A new 1D chaotic system for image encryption. Signal Process 97:172–182

    Article  Google Scholar 

  47. Zhou Y, Hua Z, Pun CM, Chen CP (2015) Cascade chaotic system with applications. IEEE Trans Cybern 45(9):2001–2012

    Article  Google Scholar 

  48. Zhu H, Zhao C, Zhang X (2013) A novel image encryption–compression scheme using hyper-chaos and Chinese remainder theorem. Signal Process-Image 28(6):670–680

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SSN College of Engineering, Chennai, India

    R. Ponuma & R. Amutha

Authors
  1. R. Ponuma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Amutha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Ponuma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ponuma, R., Amutha, R. Compressive sensing based image compression-encryption using Novel 1D-Chaotic map. Multimed Tools Appl 77, 19209–19234 (2018). https://doi.org/10.1007/s11042-017-5378-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5378-2

Keywords

Search

Navigation