Feature regions based on graph optimization for robust reversible watermarking

Recently, robust reversible watermarking (RRW) has gained increasing interests and researchers are seeking more stable image features to design watermark embedding and extraction models for local image regions protection. Previous studies show that it is promising to construct local feature regions (FRs) for RRW to handle this problem. However, selecting non-overlapping FRs and evaluating FRs stability for RRW are still challenging. To target this issue, we first construct an undirected weighted graph based on the FRs distribution pattern and then formulate the non-overlapping FRs selection as a weighted maximal clique problem and develop a maximum gain-cost ratio algorithm for its approximately optimal solution. Furthermore, we design reasonable metrics to evaluate the FRs stability in terms of FRs locations and local image content. Extensive experiments demonstrate the effectiveness and efficiency of our work.