An Improved Non-Repudiate Scheme-Feature Marking Voice Signal Communication

An Improved Non-Repudiate Scheme-Feature Marking Voice Signal Communication

Автор: Remya A R, A Sreekumar, Supriya M H, Tibin Thomas

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 2 vol.6, 2014 года.

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Guaranteeing the ownership or copyright of digital communication is of extreme importance in this digital era. Watermarking is the technique which confirms the authenticity or integrity of communication by hiding relevant information in specified areas of the original signal such that it might render it difficult to distinguish one from the other. Thus, the digital watermark can be defined as a type of indicator secretly embedded in a noise tolerant signal such as image, audio or video data. The paper presents a voice signal authentication scheme by employing signal features towards the preparation of the watermark and by embedding it in the transform domain with the Walsh transforms. Watermark used in this technique is unique for each member participating in this communication system and makes it is very imperative in the context of signal authentication.

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Digital Watermarking, FeatureMark, Walsh Transforms, Non-repudiation

Короткий адрес: https://sciup.org/15011271

IDR: 15011271

Список литературы An Improved Non-Repudiate Scheme-Feature Marking Voice Signal Communication

  • Stefan K, Fabien A P, Information hiding techniques for steganography and digital watermarking. Artech House, London, UK, 2000.
  • Wang XY, Ma TX, Niu PP (2011) A pseudo-zernike moment based audio watermarking scheme robust against desynchronization attacks. Computers & Electrical Engineering 37(4):425–443.
  • Wang XY, Niu PP, Yang HY (2009) A robust digital audio watermarking based on statistics characteristics. Pattern Recognition 42(11):3057–3064.
  • Bhat V, Sengupta I, Das A (2011) An audio watermarking scheme using singular value decomposition and dither-modulation quantization. Multimedia Tools and Applications 52(2-3):369–383.
  • Mierswa I, Morik K (2005) Automatic feature extraction for classifying audio data. Machine learning 58(2-3):127–149.
  • JongwonSeok, Jinwoo Hong, and Jinwoong Kim, A Novel Audio Watermarking Algorithm for Copyright Protection of Digital Audio, ETRI Journal, Volume 24, Number 3, June 2002.
  • Lin Y, Abdulla WH (2008) Multiple scrambling and adaptive synchronization for audio watermarking. In: Digital watermarking, Springer, pp 440–453.
  • Bassia P, Pitas I, Nikolaidis N (2001) Robust audio watermarking in the time domain. Multimedia, IEEE Transactions on 3(2):232–241.
  • Swanson MD, Zhu B, Tewfik AH, Boney L (1998) Robust audio watermarking using perceptual masking. Signal Processing 66(3):337–355.
  • Lee SK, Ho YS (2000) Digital audio watermarking in the cepstrum domain. Consumer Electronics, IEEE Transactions on 46(3):744–750.
  • Cvejic N, Sepp?nen T (2004) Spread spectrum audio watermarking using frequency hopping and attack characterization. Signal processing 84(1):207–213.
  • Yeo IK, Kim HJ (2003) Modified patchwork algorithm: A novel audio watermarking scheme. Speech and Audio Processing, IEEE Transactions on 11(4):381–386.
  • Xu X, Peng H, He C (2007) Dwt-based audio watermarking using support vector regression and subsampling. In: Applications of Fuzzy Sets Theory, Springer, pp 136–144.
  • Mali MD, Khot S (2012) Robustness test analysis of histogram based audio watermarking. In: Wireless Networks and Computational Intelligence, Springer, pp 611–620.
  • Lin Y, Abdulla WH (2008) Multiple scrambling and adaptive synchronization for audio watermarking. In: Digital watermarking, Springer, pp 440–453.
  • Wang C, Ma X, Cong X, Yin F (2005) An audio watermarking scheme with neural network. In: Advances in Neural Networks–ISNN 2005, Springer, pp 795–800.
  • Gopalan K, Audio steganography by cepstrum modification. In: Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP’05). IEEE International Conference on, IEEE, vol 5, pp v–481.
  • Gopalan K, Robust watermarking of music signals by cepstrum modification. In: Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on, IEEE, pp 4413–4416.
  • Kraetzer C, Dittmann J (2007) Mel-cepstrum based steganalysis for voip-steganography. Proceedings of SPIE, Security, Steganography, and Watermarking of Multimedia Contents IX 6505:650,505–1.
  • http://practicalcryptography.com/.
  • http://en.wikipedia.org/wiki/.
  • http://sovarr.c4dm.eecs.qmul.ac.uk/wiki/Spectral_Rolloff.
  • http://www.paradisedata.com/collateral/articles/AN_035OptimisedSpectralRollOffApplicationNote.pdf/.
  • http://www.mathworks.com/.
  • Coffey T, Saidha P (1996) Non-repudiation with mandatory proof of receipt. ACM SIGCOMM Computer Communication Review 26(1):6–17.
  • Kremer S, Markowitch O, Zhou J (2002) An intensive survey of fair non-repudiation protocols. Computer communications 25(17):1606–1621.
  • He X, Scordilis MS (2008) Efficiently synchronized spreadspectrum audio watermarking with improved psychoacoustic model. Journal of Electrical and Computer Engineering 2008.
  • http://paginas.fe.up.pt/~hmiranda/cm/Pseudo_Noise_Sequences.pdf/.
  • http://www.math.wpi.edu/MPI2008/TSC/TSCeindlijk.pdf/.
  • Tzafestas S (1983) Walsh transform theory and its application to systems analysis and control: an overview. Mathematics and Computers in Simulation 25(3):214–225.
  • Abbasi, Shuja A and Alamoud, ARM and others, Design of Real Time Walsh Transform for Processing of Multiple Digital Signals, International Journal of Electrical and Computer Engineering (IJECE), Vol. 3, No. 2, April 2013, pp. 197-206.
  • Goresky, Mark, and Andrew Klapper, Arithmetic Correlations and Walsh Transforms, IEEE Transactions On Information Theory, Vol. 58, No. 1, January 2012.
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