Rotation, scaling, and translation robust image watermarking using gabor kernels

In this paper, we propose a RST-robust watermarking algorithm which exploits the orientation feature of a host image by using 2D Gabor kernels. From the viewpoint of watermark detection, host images are usually regarded as noise. However, since geometric manipulations affect watermarks as well as the host images simultaneously, evaluating host image can be helpful to measure the nature of distortion. To make most use of this property, we first hierarchically find the orientation of the host image with 2D Gabor kernels and insert a modified reference pattern aligned to the estimated orientation in a selected transformed domain. Since the pattern is generated in a repetitive manner according, to the orientation, in its detection step, we can simply project the signal in the direction of image orientation and average the projected value to obtain a 1-D average pattern. Finally, correlation of the 1-D projection average pattern with watermark identifies periodic peaks. Analysed are experimental results against geometric attacks including aspect ratio changes and rotation.