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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)
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)
Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
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)
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)
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)
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)
Nan, L., Yanhong, S., Jiancheng, Z.: An audio scrambling method based on fibonacci transformation. J. North China Univ. Technol. 16(3), 8–11 (2004)
Satti, M., Kak, S.: Multilevel indexed quasigroup encryption for data and speech. IEEE Transactions on Broadcasting 55(2), 270–281 (2009)
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)
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)
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)
Tropp, J.A.: Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory 50(10), 2231–2242 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)