Optimum Decoder for Multiplicative Spread Spectrum Image Watermarking with Laplacian Modeling

TL;DR

This paper proposes an optimal decoding method for multiplicative spread spectrum image watermarking using Laplacian modeling, improving robustness and transparency in noisy environments.

Contribution

It introduces a maximum likelihood decoder tailored for Laplacian-distributed signals and noise, enhancing watermark detection performance.

Findings

The proposed decoder outperforms conventional methods in noisy conditions.

Laplacian modeling better fits digital media loss characteristics.

Simulations confirm improved watermark detection accuracy.

Abstract

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.