Robust digital image watermarking in curvelet domain

A robust image watermarking scheme in curvelet domain is proposed. The curvelet transform directly takes edges as the basic representation element; it provides optimally sparse representations of objects along edges. The image is partitioned into blocks and curvelet transform is applied to those blocks with strong edges. The watermark consists of a pseudorandom sequence is added to the significant curvelet coefficients. The embedding strength of watermark is constrained by a Just Noticeable Distortion model based on Barten's contrast sensitivity function. The developed JND model enables highest possible amount of information hiding without compromising the quality of the data to be protected. The watermarks are blindly detected using correlation detector. A scheme for detection and recovering geometric attacks is applied before watermark detection. The proposed scheme provides an accurate estimation of single and/or combined geometrical distortions and is relied on edge detection and radon transform. The selected threshold for watermark detection is determined on the statistical analysis over the host signals and embedding schemes. Experiments show the fidelity of the protected image is well maintained. The watermark embedded into curvelet coefficients provides high tolerance to severe image quality degradation and robustness against geometric distortions as well.