Abstract
A high payload audio watermarking technique is proposed based on the compressed sensing and sparse coding framework, with robustness to MP3 128kbps and 64kbps compression attacks. The binary watermark is a sparse vector with one non-zero element that takes a positive or negative sign based on the bit value to be encoded. A Gaussian random dictionary maps the sparse watermark to a random watermark embedding vector that is selected adaptively for each audio frame to maximize robustness to the MP3 attack. At the decoder, the Basis Pursuit Denoising algorithm (BPDN) is used to extract the embedded watermark sign. High payloads of (689, 1378 and 2756) bps are achieved with %BER of (0.3%, 0.5% and 1%) and (0.1%, 0.3% and 0.5%) for 64kbps and 128kbps MP3 compression attacks respectively. The signal to embedding noise ratio is kept in the range of 27-30 dB in all cases.
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References
Noriega, R.M., Nakano, M., Kurkoski, B., Yamaguchi, K.: High Payload Audio Watermarking: toward Channel Characterization of MP3 Compression. Journal of Information Hiding and Multimedia Signal Processing 2(2), 91–107 (2011)
Vivekananda, B.K., Indranil, S., Abhijit, D.: An Audio Watermarking Scheme using Singular Value Decomposition and Dither-Modulation Quantization. Multimedia Tools and Applications Journal 52(2-3), 369–383 (2011)
Dhavale, S.V., Deodhar, R.S., Patnaik, L.M.: Walsh Hadamard Transform Based Blind Watermarking for Digital Audio Copyright Protection. In: Das, V.V., Thankachan, N. (eds.) CIIT 2011. CCIS, vol. 250, pp. 469–475. Springer, Heidelberg (2011)
Yang, H., Bao, D., Wang, X., Niu, P.: A Robust Content Based Audio Watermarking using UDWT and Invariant Histogram. Multimedia Tools and Applications Journal (November 2010)
El Hamdouni N., Adib A., Labri S., Torki M.: A Blind Digital Audio Watermarking Scheme Based on EMD and UISA Techniques. Multimedia Tools and Applications Journal (January 2012)
Tewari, T.K., Saxena, V., Gupta, J.P.: Audio Watermarking: Current State of Art and Future Objectives. International Journal of Digital Content Technology and Applications 5(7), 306–313 (2011)
Datta, K., Gupta, I.S.: Partial Encryption and Watermarking Scheme for Audio Files with Controlled Degradation of Quality. Multimedia Tools and Applications, Journal (2012)
Ercelebi, E., Batakci, L.: Audio watermarking Scheme Based on Embedding Strategy in Low Frequency Components with a Binary Image. Digital Signal Processing 19(2), 265–277 (2009)
Orsdemir, A., Altun, H.O., Sharma, G., Bocko, M.F.: On the Security and Robustness of Encryption via Compressed Sensing. In: IEEE Military Communication Conference MILCOM 2008, pp. 1–7 (2008)
Candès, E., Tao, T.: Decoding by Linear Programming. IEEE Transaction on Information Theory 51(12), 4203–4215 (2005)
Candès, E., Randall, P.: Highly Robust Error Correction by Convex Programming. IEEE Transaction on Information Theory 54(7) (2006)
Laska, J., Davenport, M., Baraniuk, R.: Exact Signal Recovery from Sparsely Corrupted Measurements through the Pursuit of Justice. In: Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, California (2009)
Gemmeke, J.F., Virtanen, T., Hurmalainen, A.: Examplar Based Sparse Representations for Noise Robust Automatic Speech Recognition. IEEE Trans. Audio, Speech and Language Processing 19(9), 2067–2080 (2011)
Sprechman, P., Sapiro, G.: Dictionary Learning and Sparse Coding for Unsupervised Clustering. In: ICASSP 2010, pp. 2042–2045 (2010)
Wright, J., Yi, M., Mairal, J., Sapiro, G., Huang, T., Yan, S.: Sparse Representation for Computer Vision and Pattern Recognition. Proc. of IEEE 98(6), 1031–1044 (2010)
Sheikh, M., Baraniuk, R.: Blind Error-Free Detection of Transform-Domain Watermarks. In: IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, vol. 5, pp. V-453–V-456 (September 2007)
Tagliasacchi, M., Valenzise, G., Tubaro, S.: Hash-Based Identification of Sparse Image Tampering. IEEE Transactions on Image Processing 18(11), 2491–2504 (2009)
Valenzise, G., Prandi, G., Tagliasacchi, M., Sarti, A.: Identification of Sparse Audio Tampering using Distributed Source Coding and Compressive Sensing Techniques. Eurasip Journal on Image and Video Processing 2009, 1–13 (2009)
Fakhr, M.W.: Robust Watermarking using Compressed Sensing Framework with Application to MP3. International Journal of Multimedia and its Applications, IJMA 4(6), 27–43 (2012)
Fakhr, M.W.: Sparse Watermark Embedding and Recovery using Compressed Sensing Framework for Audio Signals. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Sanya, China, pp. 535–539 (2012)
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Fakhr, M.W. (2013). High Payload Audio Watermarking Using Sparse Coding with Robustness to MP3 Compression. In: Awad, A.I., Hassanien, A.E., Baba, K. (eds) Advances in Security of Information and Communication Networks. SecNet 2013. Communications in Computer and Information Science, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40597-6_8
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DOI: https://doi.org/10.1007/978-3-642-40597-6_8
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