A universally optimum decoder for multiplicative audio watermarking

In this paper, we propose a novel detector for multiplicative watermarking. The decoder extracts the watermark data by comparing the variances of the watermarked signal and the original signal. Due to the use of the variance test, the decoder works independent of the distribution of the host signal. This is the major advantage of this decoder per other decoders. The decoder is optimized under the Additive White Gaussian Noise channel by calculating the best threshold value. In order to show the optimal performance of this decoder on audio signals, a Maximum likelihood (ML) decoder is also introduced by considering a Gaussian distribution for the host signal. Furthermore, PEAQ algorithm is used for controlling the inaudibility of the watermark data insertion. Using this algorithm, the watermark strength factor updates automatically every 200 ms, according to the quality which is desired for the watermarked signal. Simulation results showed that the proposed decoder is extremely robust to the common audio watermarking attacks and slightly better than the ML decoder under Additive White Gaussian Noise attack.