A GA-Based Joint Coding and Embedding Optimization for Robust and High Capacity Image Watermarking

Abstract

A new informed image watermarking algorithm is presented in this paper, which can achieve the information rate of 1/64 bits/pixel with high robustness. Firstly, a LOT (local optimal test) detector based on HMM in wavelet domain is developed to tackle the issue that the exact strength for informed embedding is unknown to the receiver. Then based on the LOT detector, the dirty-paper code for informed coding is constructed and the metric for the robustness is defined accordingly. Unlike the previous approaches of informed watermarking which take the informed coding and embedding process separately, the proposed algorithm implements a joint coding and embedding optimization for high capacity and robust watermarking. The genetic algorithm (GA) is employed to optimize the robustness and distortion constraints simultaneously. Experimental results show that the proposed algorithm achieves significant improvements in performance against JPEG, gain attack, low-pass filtering and etc.