Robust multiplicative watermarking technique with Maximum Likelihood detector

In this paper, a new multiplicative semi-blind image-adaptive watermarking system has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. To have a better robustness, the proposed algorithm is implemented on the low-frequency coefficients of the wavelet transform. Moreover, regarding invisibility of the algorithm, the strength factor which controls the watermark power is optimally selected. To extract the watermark data, the Maximum Likelihood (ML) estimator is used. Experimental results confirm the imperceptibility of the proposed method and its high robustness against various attacks such as JPEG compression, noise addition, and filtering compared to similar methods reported so far.