Advances in Audio Watermarking Based on Singular Value Decomposition

The recent development in computational world and the wide availability of internet have facilitated the transmission and distribution of multimedia content. As a result, the protection of intellectual property rights of digital content has been the key problem. Digital watermarking has drawn extensive attention for protecting digital contents from unauthorized copying. It is a process of embedding watermark into the original content to show authenticity and ownership. It has been widely used for several purposes including copyright protection, information carrier, broadcast monitoring, fingerprinting, data authentication, medical safety, and so on.

The previous chapter briefly discussed the application and properties of digital watermarking, outlined the motivation, and the organization of the book. This chapter presents some existing popular audio watermarking methods and some transformation and decomposition techniques used in the proposed watermarking methods.

This chapter presents a DWT-DCT-based audio watermarking method using SVD and quantization [22, 23]. In the proposed method, initially the original audio signal is segmented into non-overlapping frames. DWT is applied to each frame and detail coefficients are represented in matrix form. DCT is performed on the detail coefficients and the obtained DCT coefficients are reshaped. SVD is applied to the reshaped DCT coefficients of each frame. Watermark information is then embedded into the highest singular value of each audio frame by quantization. Watermark information is extracted by comparing the largest singular value of original and attacked watermarked DCT coefficients obtained from the DWT sub bands of each audio frame. Experimental results indicate that our proposed watermarking method is highly robust against various attacks such as noise addition, cropping, resampling, requantization, and MP3 compression. Moreover, it outperforms state-of-the-art watermarking methods in terms of imperceptibility, robustness, and data payload. The SNR values of the proposed method range from 38 to 41 dB, in contrast to the state-of-the-art methods whose SNR values range from only 12 to 28 dB. In addition, the maximum bit error rate (BER) of the proposed method is 3.3203, whereas the maximum BER of the state-of-the-art methods is 51.73. Moreover, the data payload of the proposed method is 172.39 bps, which is relatively higher than that of the state-of-the-art methods.

In this chapter, we present a FFT-based audio watermarking method using SVD and CPT [22, 24]. In our proposed method, watermark information is pre-processed first using a Gaussian map in order to improve the robustness and enhance the confidentiality. Then, the original audio is segmented into nonoverlapping frames. FFT is applied to each frame and low frequency FFT coefficients are selected. SVD is applied to the selected FFT coefficients of each frame represented in a matrix form. The highest two singular values of each frame are selected. The selected singular values are assumed as the components of polar coordinate system and are transformed into the components of Cartesian coordinate system. Watermark information is embedded into each of these Cartesian components using an embedding function. Experimental results demonstrate that our proposed watermarking method resists various attacks such as noise addition, cropping, resampling, requantization, and MP3 compression. Moreover, it outperforms the state-of-the-art watermarking algorithms in terms of imperceptibility, robustness, and data payload. The SNR and MOS values of the proposed method range from 36.19 to 37.32 dB and 4.95 to 5.0, respectively. This is in contrast to the above state-of-the-art methods whose SNR and MOS values range from only 12.87 to 29.50 dB and 2.93 to 4.7, respectively. Moreover, the BER of the proposed method ranges from 0 to 2.9297, whereas the BER of the state-of-the-art methods ranges from 0 to 51.73. Furthermore, the data payload of the proposed method is 689.56 bps which is relatively higher than that of the state-of-the-art methods.

This chapter concludes this book with a brief summary of our research work. The future research work is also discussed in this chapter.