Imperceptibility, robustness, and payload are three main requirements of any audio watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. Generally, in order to enhance the imperceptibility, robustness, and payload simultaneously, the human auditory system and the statistical properties of the audio signal should be fully taken into account. The statistical model based transform domain multiplicative watermarking scheme embodies the above ideas, and therefore the detection and extraction of the multiplicative watermarks have received a great deal of attention. Although much effort has been made in recent years, improving the ability of imperceptibility, watermark capacity, and robustness at the same time remains a challenge within the audio watermarking community. In this paper, we propose a blind audio watermark decoder in stationary wavelet transform domain based on bivariate generalized Gaussian distributions, wherein both the local statistical properties and inter-scale dependencies of the stationary wavelet transform coefficients of digital audio are taken into account, and also the adaptive nonlinear watermark embedding strength functions are designed. The results of our tests with different host audios, digital watermarks, and various attacks, we experimentally confirm that the proposed approach performs well compared to the state-of-the-art audio watermarking methods.
