Optimum Decoder for Multiplicative Spread Spectrum Image Watermarking with Laplacian Modeling
This work addresses image watermarking for digital media protection, but it is incremental as it modifies an existing method with a different statistical model.
The paper tackled the problem of multiplicative spread spectrum image watermarking by modeling signal and noise with Laplacian distributions instead of Gaussian, deriving an optimum decoder via maximum likelihood decoding. The results showed suitable performance and transparency for watermarking applications, though no concrete numbers were provided.
This paper investigates the multiplicative spread spectrum watermarking method for the image. The information bit is spreaded into middle-frequency Discrete Cosine Transform (DCT) coefficients of each block of an image using a generated pseudo-random sequence. Unlike the conventional signal modeling, we suppose that both signal and noise are distributed with Laplacian distribution because the sample loss of digital media can be better modeled with this distribution than the Gaussian one. We derive the optimum decoder for the proposed embedding method thanks to the maximum likelihood decoding scheme. We also analyze our watermarking system in the presence of noise and provide analytical evaluations and several simulations. The results show that it has the suitable performance and transparency required for watermarking applications.