Audio Watermarking Based on FWHT and LUD

Abstract

This chapter introduces an audio watermarking algorithm based on FWHT and LU decomposition (LUD). To the best of our knowledge, this is the first audio watermarking method based on FWHT, LUD, and quantization jointly. Initially, we preprocess the watermark data to enhance the security of the proposed algorithm. Then, the original audio is segmented into nonoverlapping frames and FWHT is applied to each frame. LUD is applied to the FWHT coefficients represented in a matrix form. Watermark data are embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame. Experimental results indicate that proposed algorithm is considerably robust and reliable against various attacks without degrading the quality of the watermarked audio. Moreover, it shows more excellent results than the state-of-the-art methods in terms of imperceptibility, robustness, and data payload. The main limitation of the existing audio watermarking techniques is the difficulty to obtain a favorable trade-off among imperceptibility, robustness, and data payload. To overcome this limitation, in this chapter, we propose a blind audio watermarking algorithm based on fast Walsh-Hadamard transform (FWHT) and LU decomposition (LUD). The main features of the proposed scheme are: (i) it utilizes the FWHT, LUD, and quantization jointly; (ii) it uses a tent map that contains the chaotic characteristic to enhance the confidentiality of the proposed algorithm; (iii) watermark is embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame by quantization; (iv) watermark extraction process is blind; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking algorithm shows high robustness against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 15–16, 20, 22–23, 26, 28] in terms of imperceptibility, robustness, and data payload.