Scrambling-based speech encryption via compressed sensing

Conventional speech scramblers have three disadvantages, including heavy communication overhead, signal features underexploitation, and low attack resistance. In this study, we propose a scrambling-based speech encryption scheme via compressed sensing (CS). Distinguished from conventional scramblers, the above problems are solved in a unified framework by utilizing the advantages of CS. The presented encryption idea is general and easily applies to speech communication systems. Compared with the state-of-the-art methods, the proposed scheme provides lower residual intelligibility and greater cryptanalytic efforts. Meanwhile, it ensures desirable channel usage and notable resistibility to hostile attack. Extensive experimental results also confirm the effectiveness of the proposed scheme.

[1]  Tingting Xu,et al.  Novel Speech Secure Communication System Based on Information Hiding and Compressed Sensing , 2009, 2009 Fourth International Conference on Systems and Networks Communications.

[2]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[3]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[4]  Qiyu Sun.  Sparse Approximation Property and Stable Recovery of Sparse Signals From Noisy Measurements , 2011, IEEE Transactions on Signal Processing.

[5]  Jesús Navarro-Moreno,et al.  Nonlinear estimation using correlation information , 2006, IEEE Transactions on Signal Processing.

[6]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[7]  Joseph A. O'Sullivan,et al.  Information-theoretic analysis of information hiding , 2003, IEEE Trans. Inf. Theory.

[8]  Joel A. Tropp,et al.  Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.

[9]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[10]  John A. Clark,et al.  Nature-inspired cryptography: past, present and future , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[11]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[12]  Juan Carlos De Martin,et al.  Perception-based partial encryption of compressed speech , 2002, IEEE Trans. Speech Audio Process..

[13]  Huan Li,et al.  Audio Scrambling Algorithm Based on Variable Dimension Space , 2009, 2009 International Conference on Industrial and Information Systems.

[14]  Zachary M. Smith,et al.  Chimaeric sounds reveal dichotomies in auditory perception , 2002, Nature.

[15]  Subhash C. Kak,et al.  Multilevel Indexed Quasigroup Encryption for Data and Speech , 2009, IEEE Transactions on Broadcasting.

[16]  Lin Zhao,et al.  Chaos synchronization based on observer and its application in speech secure communication , 2010, 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content.

[17]  D. Bendor,et al.  The neuronal representation of pitch in primate auditory cortex , 2005, Nature.

[18]  V. Senk,et al.  A new speech scrambling concept based on Hadamard matrices , 1997, IEEE Signal Processing Letters.

[19]  Tang Yuan-sheng.  Secure communication based on network coding , 2010 .

[20]  T. Yin,et al.  Envelope coding in the lateral superior olive. I. Sensitivity to interaural time differences. , 1995, Journal of neurophysiology.

[21]  Romano Fantacci,et al.  A new speech signal scrambling method for secure communications: theory, implementation, and security evaluation , 1989, IEEE J. Sel. Areas Commun..

[22]  K. Saberi,et al.  Cognitive restoration of reversed speech , 1999, Nature.

[23]  Saibal K. Pal.  Fast, Reliable & Secure Digital Communication Using Hadamard Matrices , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[24]  Günes Karabulut-Kurt,et al.  Perceptual audio features for emotion detection , 2012, EURASIP J. Audio Speech Music. Process..

[25]  Fabian J. Theis,et al.  Sparse component analysis and blind source separation of underdetermined mixtures , 2005, IEEE Transactions on Neural Networks.

[26]  Shao Liping.  An n-Dimensional Space Audio Scrambling Algorithm Based on Random Matrix , 2010 .

[27]  Christian Jutten,et al.  On the Stable Recovery of the Sparsest Overcomplete Representations in Presence of Noise , 2010, IEEE Transactions on Signal Processing.

[28]  Hamid Sharif,et al.  Index-Based Selective Audio Encryption for Wireless Multimedia Sensor Networks , 2010, IEEE Transactions on Multimedia.

[29]  Tian-Jing Wang,et al.  A Speech Signal Sparse Representation Algorithm Based on Adaptive Overcomplete Dictionary: A Speech Signal Sparse Representation Algorithm Based on Adaptive Overcomplete Dictionary , 2011 .