Backward-forward distortion minimization for binary images data hiding
In our previous paper, we propose to use the morphological transform for binary images data hiding for authentication purpose. We view flipping an edge pixel in binary images as shifting the edge location one pixel horizontally and vertically. Based on this observation, we propose an interlaced morphological binary wavelet transform to track the shifted edges. The two processing cases that flipping the candidates of one does not affect the flippability conditions of another are employed such that a large capacity can be achieved. However, employing double processing cases sacrifices the visual quality of the watermarked image. In this paper, we further investigate and propose a novel effective Backward-Forward Minimization method to minimize the visual distortion for double processing cases. Experimental results demonstrate its superior performance.