Artificial Intelligence Approach for Tuning Speech-Adaptive Watermarking using Higher-Order Statistics (HOS)

The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration.