Abstract: Rapid growth in information technology and computer networks has resulted in the universal use of data transmission in the digital domain. However, the major challenge faced by digital data owners is protection of data against unauthorized copying and distribution. Digital watermark technology is starting to be considered a credible protection method to mitigate the potential challenges that undermine the efficiency of the system. Digital audio watermarking should retain the quality of the host signal in a way that remains inaudible to the human hearing system. It should be sufficiently robust to be resistant against potential attacks. One of the major deficiencies of conventional audio watermarking techniques is the use of non-intelligent decoders in which some sets of specific rules are used for watermark extraction. This paper presents a new robust intelligent audio watermarking scheme using a synergistic combination of singular value decomposition (SVD) and support vector machine (SVM). The methodology involves embedding a watermark data by modulating the singular values in the SVD transform domain. In the extraction process, an intelligent detector using SVM is suggested for extracting the watermark data. By learning the destructive effects of noise, the detector in question can effectively retrieve the watermark. Diverse experiments under various conditions have been carried out to verify the performance of the proposed scheme. Experimental results showed better imperceptibility, higher robustness, lower payload, and higher operational efficiency, for the proposed method than for conventional techniques.

运用支持向量机的稳健智能音频水印设计

[1]Abd El-Samie, F.E., 2009. An efficient singular value decomposition algorithm for digital audio watermarking. Int. J. Speech Technol., 12(1):27-45.

[2]Acevedo, A.G., 2006. Audio watermarking quality evaluation. In: E-business and Telecommunication Networks. Springer, p.272-283.

[3]Arnold, M., 2000. Audio watermarking: features, applications, and algorithms. IEEE Int. Conf. on Multimedia and Expo, p.1013-1016.

[4]Bhat, K.V., Sengupta, I., Das, A., 2010. An adaptive audio watermarking based on the singular value decomposition in the wavelet domain. Dig. Signal Process., 20:1547- 1558.

[5]Bhat, K.V., Sengupta, I., Das, A., 2011. An audio watermarking scheme using singular value decomposition and dither- modulation quantization. Multim. Tools Appl., 52(2):369- 383.

[6]Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn., 20(3):273-297.

[7]Cox, I., Miller, M., Bloom, J., et al., 2007. Digital Watermarking and Steganography. Morgan Kaufmann, USA.

[8]Dhar, P.K., Shimamura, T., 2015. Blind SVD-based audio watermarking using entropy and log-polar transformation. J. Inform. Secur. Appl., 20:74-83.

[9]Dutta, M.K., Pathak, V.K., Gupta, P., 2010. A robust watermarking algorithm for audio signals using SVD. Int. Conf. on Cotemporary Computing, p.84-93.

[10]Fan, M., Wang, H., 2009. Chaos-based discrete fractional Sine transform domain audio watermarking scheme. Comput. Electr. Eng., 35(3):506-516.

[11]Fu, G., Peng, H., 2007. Subsampling-based wavelet watermarking algorithm using support vector regression. Int. Conf. on “Computer as a Tool”, p.138-141.

[12]Hartung, F., Kutter, M., 1999. Multimedia watermarking techniques. Proc. IEEE, 87(7):1079-1107.

[13]Hu, H.T., Hsu, L.Y., Chou, H.H., 2014. Perceptual-based DWPT-DCT framework for selective blind audio watermarking. Signal Process., 105:316-327.

[14]Kabal, P., 2002. An Examination and Interpretation of ITU-R BS. 1387: Perceptual Evaluation of Audio Quality. TSP Lab Technical Report, Department of Electrical & Computer Engineering, McGill University.

[15]Lang, I.A., 2005. Stirmark Benchmark for Audio (SMBA): Evaluation of Watermarking Schemes for Audio. Version 1.3.1.

[16]Lei, B., Soon, I., Zhou, F., et al., 2012. A robust audio watermarking scheme based on lifting wavelet transform and singular value decomposition. Signal Process., 92(9): 1985-2001.

[17]Lei, B., Song, I., Rahman, S.A., 2013. Robust and secure watermarking scheme for breath sound. J. Syst. Softw., 86(6):1638-1649.

[18]Mohsenfar, S.M., Mosleh, M., Barati, A., 2013. Audio watermarking method using QR decomposition and genetic algorithm. Multim. Tools Appl., 74(3):759-779.

[19]Peng, H., Wang, J., Wang, W., 2010. Image watermarking method in multiwavelet domain based on support vector machines. J. Syst. Softw., 83(8):1470-1477.

[20]Peng, H., Li, B., Luo, X., et al., 2013. A learning-based audio watermarking scheme using kernel Fisher discriminant analysis. Dig. Signal Process., 23(1):382-389.

[21]Tao, Z., Zhao, H.M., Wu, J., et al., 2010. A lifting wavelet domain audio watermarking algorithm based on the statistical characteristics of sub-band coefficients. Arch. Acoust., 35(4):481-491.

[22]Trefethen, L.N., Bau, D.III, 1997. Numerical Linear Algebra. SIAM.

[23]Tsai, H.H., Sun, D.W., 2007. Color image watermark extraction based on support vector machines. Inform. Sci., 177(2): 550-569.

[24]Tsougenis, E., Papakostas, G.A., Koulouriotis, D.E., et al., 2012. Performance evaluation of moment-based water- marking methods: a review. J. Syst. Softw., 85(8):1864- 1884.

[25]Wang, J., Lin, F.Z., 2005. Digital audio watermarking based on support vector machine. J. Comput. Res. Dev., 42(9): 1605-1611 (in Chinese).

[26]Wang, X.Y., Niu, P.P., Qi, W., 2008. A new adaptive digital audio watermarking based on support vector machine. J. Netw. Comput. Appl., 31(4):735-749.

[27]Wang, X.Y., Ma, T.X., Niu, P.P., 2011a. A pseudo-Zernike moment based audio watermarking scheme robust against desynchronization attacks. Comput. Electr. Eng., 37(4): 425-443.

[28]Wang, X.Y., Niu, P.P., Lu, M.Y., 2011b. A robust digital audio watermarking scheme using wavelet moment invariance. J. Syst. Softw., 84(8):1408-1421.

[29]Yen, S.H., Wang, C.J., 2006. SVM based watermarking technique. Tamkang J. Sci. Eng., 9(2):141-150.