Proc. SPIE. 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII
KEYWORDS: Signal to noise ratio, Sensors, Fourier transforms, Digital watermarking, Signal processing, Electronic filtering, Signal detection, Statistical modeling, Systems modeling, Filtering (signal processing)
One requirement for audio watermarks is that the embedded watermark should be imperceptible and does not alter the audio signal quality. To achieve this goal, existing audio watermarking methods use a power constraint or more sophisticated Human Auditory System (HAS) models. At the embedding side the watermark signal is shaped by a masking curve computed on the original signal. At the detector, signal processing like Wiener filtering or inverse filtering whitens the watermark and tries to avoid host signal effect. Then, the correlation detector, which is the Maximum Likelihood (ML) optimal detector, is applied considering Gaussian assumption for the signals. The method described in this paper uses a different approach in the DFT domain. A new ML detector is derived assuming a Weibull distribution for the modulus of the Discrete Fourier Transform of the host signal. Performances of the new proposed detector are given and compared to the correlation detector that assumes a Gaussian distribution of the signal.