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Narrowband Speech Hiding Using Vector Quantization

Authors: Driss Guerchi; Fatiha Djebbar;

Narrowband Speech Hiding Using Vector Quantization

Abstract

{"references": ["N. Johnson and S. Jajodia, \"Exploring steganography: seeing the unseen,\"\nIEEE Computer, pp. 26-34, February 1998.", "Eugene T. Lin and Edward J. Delp \"A review of data hiding in digital\nimages\", Proceedings of the Image Processing, Image Quality, Image\nCapture Systems Conference, PICS-99", "T. Rabie, A Novel Compression Technique for Super Resolution Color\nPhotography, IEEE International Conference on Innovations in Information\nTechnology (IIT2006), November 2006. Dubai, UAE, 1-5.", "D. Guerchi, H. M. Harmain, T. Rabie, and E. E. Mohamed, \"Speech Secrecy:\nAn FFT-based Approach,\" Special Issue on \"Evolving Computer\nScience Applications\" of the Journal of Mathematics and Computer\nScience, 3, n.2, pp. 107-125, 2008.", "D. Guerchi, \"LPC-based Narrowband Speech Steganography,\" Journal\nof Multimedia. (To appear.)", "D. O-Shaughnessy, \"Speech Communications: Human and Machine,\"\nWiley-IEEE Press; 2 edition, 1999.", "F. Itakura, \"Line spectrum representation of linear predictive coefficients,\"\nJournal of Acoustics Society of America, vol. 57, no. 1, pp.\nS35, 1975."]}

In this work we introduce an efficient method to limit the impact of the hiding process on the quality of the cover speech. Vector quantization of the speech spectral information reduces drastically the number of the secret speech parameters to be embedded in the cover signal. Compared to scalar hiding, vector quantization hiding technique provides a stego signal that is indistinguishable from the cover speech. The objective and subjective performance measures reveal that the current hiding technique attracts no suspicion about the presence of the secret message in the stego speech, while being able to recover an intelligible copy of the secret message at the receiver side.

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Keywords

Speech steganography, LSF vector quantization, fast Fourier transform

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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