Skip to main content

Compressive Sensing Based Audio Scrambling Using Arnold Transform

  • Conference paper
Recent Trends in Computer Networks and Distributed Systems Security (SNDS 2014)

Abstract

In this paper, a novel idea for scrambling the compressive sensed audio data using two dimensional Arnold transform is presented. In the proposed method, Arnold matrix is constructed by the numbers generated by using a secret key and a logistic map. A key based measurement matrix is used for compressive sensing to avoid the transmission and storage requirement of the matrix and to improve the security. The combination of compressive sensing and arnold scrambling provides very high security and ensures efficient channel usage, resistivity to noise, best signal to noise ratio and good scrambling of data. Experimental results confirm the effectiveness of the proposed scheme.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)

    Article  Google Scholar 

  2. Del Re, E., Fantacci, R., Maffucci, D.: A new speech signal scrambling method for secure communications: theory, implementation, and security evaluation. IEEE Journal on Selected Areas in Communications 7(4), 474–480 (1989)

    Article  Google Scholar 

  3. Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  4. Huang, R., Rhee, K.H., Uchida, S.: A parallel image encryption method based on compressive sensing. In: Multimedia Tools and Applications, pp. 1–23 (2012)

    Google Scholar 

  5. Li, H., Qin, Z., Zhang, X., Shao, L.: An n-dimensional space audio scrambling algorithm based on random matrix. Journal of Xi’an Jiaotong University 4, 005 (2010)

    Google Scholar 

  6. Lin, Y., Abdulla, W.H.: A secure and robust audio watermarking scheme using multiple scrambling and adaptive synchronization. In: 2007 6th International Conference on Information, Communications & Signal Processing, pp. 1–5. IEEE (2007)

    Google Scholar 

  7. Madain, A., Dalhoum, A.L.A., Hiary, H., Ortega, A., Alfonseca, M.: Audio scrambling technique based on cellular automata. In: Multimedia Tools and Applications, pp. 1–20 (2012)

    Google Scholar 

  8. Nan, L., Yanhong, S., Jiancheng, Z.: An audio scrambling method based on fibonacci transformation. J. North China Univ. Technol. 16(3), 8–11 (2004)

    Google Scholar 

  9. Satti, M., Kak, S.: Multilevel indexed quasigroup encryption for data and speech. IEEE Transactions on Broadcasting 55(2), 270–281 (2009)

    Article  Google Scholar 

  10. Senk, V., Delic, V.D., Milosevic, V.S.: A new speech scrambling concept based on hadamard matrices. IEEE Signal Processing Letters 4(6), 161–163 (1997)

    Article  Google Scholar 

  11. Servetti, A., De Martin, J.C.: Perception-based partial encryption of compressed speech. IEEE Transactions on Speech and Audio Processing 10(8), 637–643 (2002)

    Article  Google Scholar 

  12. Shang, Z., Ren, H., Zhang, J.: A block location scrambling algorithm of digital image based on arnold transformation. In: The 9th International Conference for Young Computer Scientists, ICYCS 2008, pp. 2942–2947. IEEE (2008)

    Google Scholar 

  13. Tropp, J.A.: Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory 50(10), 2231–2242 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Augustine, N., George, S.N., Deepthi, P.P. (2014). Compressive Sensing Based Audio Scrambling Using Arnold Transform. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54525-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54524-5

  • Online ISBN: 978-3-642-54525-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics