Skip to main content
Log in

Encryption of image data using compressive sensing and chaotic system

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

Abstract

An image cryptosystem using chaotic compressive sensing is designed to achieve simultaneous compression - encryption. Compressive sensing requires a measurement matrix to compressively sample a sparse signal and to guarantee its recovery at the receiver. In this paper, a new one-dimensional chaotic map is proposed which is used to construct the chaotic measurement matrix. Performance analysis demonstrates that the proposed chaotic map is highly chaotic, ergodic, highly sensitive to the initial conditions and suitable for chaotic compressive sensing. The parameters of the chaotic system are used as the secret key in the construction of measurement matrix and also the masking matrix. The sparse representation of the image is obtained using discrete wavelet transform. The sparse coefficients are then compressively sampled and encrypted using the chaotic measurement matrix and masking matrix. A parallel compressive sensing framework is employed which greatly improves the efficiency of the proposed chaotic compressive sensing scheme. Simulation results shows that the proposed scheme has good security performance against various attacks and better reconstruction performance, when compared with the commonly used random measurement matrix.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Badve O, Gupta BB, Gupta S (2016) Reviewing the security features in contemporary security policies and models for multiple platforms. In Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. 479–504

  2. Bandeira AS, Dobriban E, Mixon DG, Sawin WF (2013) Certifying the restricted isometry property is hard. IEEE Trans Inf Theory 59:3448–3450

    Article  MathSciNet  Google Scholar 

  3. Belazi A, Abd El-Latif AA, Belghith S (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 Secur 10:2182–2195

    Article  Google Scholar 

  5. Candes EJ (2008) The restricted isometry property and its implications for compressed sensing. Comptes Rendus Math 346:589–592

    Article  MathSciNet  Google Scholar 

  6. Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25:21–30

    Article  Google Scholar 

  7. Chen X, Huang X, Li J, Ma J, Lou W, Wong DS (2015) New algorithms for secure outsourcing of large-scale systems of linear equations. IEEE Trans Inf Forensics Secur 10:69–78

    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 (ICNSC 2014): 360–364

  9. 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 76:10097–10117

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. Fay R (2016) Introducing the counter mode of operation to compressed sensing based encryption. Inf Process Lett 116:279–283

    Article  MathSciNet  Google Scholar 

  12. Fay R, Ruland C (2016) Compressive Sensing encryption modes and their security. In 11th International Conference for Internet Technology and Secured Transactions (ICITST 2016):119–126

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

  14. 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 Represent 44:116–127

    Article  Google Scholar 

  15. Hu G, Xiao D, Wang Y, Xiang T, Zhou Q (2017) Securing image information using double random phase encoding and parallel compressive sensing with updated sampling processes. Opt Lasers Eng 98:123–133

    Article  Google Scholar 

  16. Huang Z, Liu S, Mao X, Chen K, Li J (2017) Insight of the protection for data security under selective opening attacks. Inf Sci 412–413:223–241

    Article  Google Scholar 

  17. Li J, Huang X, Li J, Chen X, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25:2201–2210

    Article  Google Scholar 

  18. Li J, Li J, Chen X, Jia C, Lou W (2015) Identity-based encryption with outsourced revocation in cloud computing. IEEE Trans Comput 64:425–437

    Article  MathSciNet  Google Scholar 

  19. Li J, Li YK, Chen X, Lee PP, Lou W (2015) A hybrid cloud approach for secure authorized deduplication. IEEE Trans Parallel Distrib Syst 26(5):1206–1216

    Article  Google Scholar 

  20. Li P, Li J, Huang Z, Tong G, Chong Z, Yiu SM, Chen K (2017) Multi-key privacy-preserving deep learning in cloud computing. Futur Gener Comput 74:76–85

    Article  Google Scholar 

  21. Mahesh M, Srinivasan D, Kankanala M, Amutha R (2015) Image cryptography using Discrete Haar Wavelet transform and Arnold Cat Map. In International Conference on Communication and Signal Processing (ICCSP 2015):1849–1855

  22. Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for Overcomplete sparse decomposition based on smoothed L0 norm. IEEE Trans Signal Process 57:289–301

    Article  MathSciNet  Google Scholar 

  23. Peng H, Tian Y, Kurths J, Li L, Yang Y, Wang D (2017) Secure and energy-efficient data transmission system based on chaotic compressive sensing in body-to-body networks. IEEE Trans Biomed Circuits Syst 11:558–573

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  26. Ponnaian D, Chandranbabu K (2017) Crypt analysis of an image compression--encryption algorithm and a modified scheme using compressive sensing. Opt J Light Electron Opt 147:263–276

    Article  Google Scholar 

  27. Ponuma R, Amutha R (2017) Compressive sensing-based image compression-encryption using novel 1D-chaotic map. Multimed Tools Appl:1–26

  28. Ponuma R, Aarthi V, Amutha R (2016) Cosine Number Transform based hybrid image compression-encryption. In IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2016):172–176

  29. Pudi V, Chattopadhyay A, Lam K-Y (2017) Secure and lightweight compressive sensing using stream cipher. IEEE Trans Circuits Syst II: Express Briefs:371–375

    Article  Google Scholar 

  30. Wu Y, Zhou Y, Saveriades G et al (2013) Local Shannon entropy measure with statistical tests for image randomness. Inf Sci 222:323–342

    Article  MathSciNet  Google Scholar 

  31. Yang Z, Yan W, Xiang Y (2015) On the security of compressed sensing-based signal cryptosystem. IEEE Trans Emerg Top Comput 3:363–371

    Article  Google Scholar 

  32. Yaseen Q, Aldwairi M, Jararweh Y, Al-Ayyoub M, Gupta B (2018) Collusion attacks mitigation in internet of things: a fog based model. Multimed Tools Appl 77:18249–18268

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Yu C, Li J, Li X, Ren X, Gupta BB (2018) Four-image encryption scheme based on quaternion Fresnel transform, chaos and computer generated hologram. Multimed Tools Appl 77:4585–4608

    Article  Google Scholar 

  35. Zhang LY, Wong K-W, Zhang Y, Zhou J (2016) Bi-level protected compressive sampling. IEEE Trans Multimed 18:1720–1732

    Article  Google Scholar 

  36. Zhang Y, Zhang LEOYU, Zhou J, Liu L, Chen FEI, He X (2016) A review of compressive sensing in information security field. 2507–2519

    Article  Google Scholar 

  37. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Optik novel hybrid image compression – encryption algorithm based on compressive sensing. Opt - Int J Light Electron Opt 125:5075–5080

    Article  Google Scholar 

  38. Zhou Y, Hua Z, Pun C-M, Chen CLP (2015) Cascade chaotic system with applications. IEEE Trans Cybern 45:2001–2012

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Ponuma.

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

Ponuma, R., Amutha, R. Encryption of image data using compressive sensing and chaotic system. Multimed Tools Appl 78, 11857–11881 (2019). https://doi.org/10.1007/s11042-018-6745-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6745-3

Keywords

Navigation