Image watermarking in the Hermite transform domain with resistance to geometric distortions

This paper proposes a novel perceptual watermarking scheme operating in a Hermite transform domain. To achieve ail acceptable level of watermark invisibility, masking properties of the Human Vision system (HVS) are exploited in the extraction of relevant local image features (texture, smooth regions, edges) for watermark embedding purpose. Many other works suggest the use of wavelets or contourlets. In our case, image features are extracted efficiently from the Hermite transform image representation which agrees with the Gaussian derivative model of the human visual perception. The resulting weighing mask is used to adapt the watermark strength to image regions during the embedding process. In order to ensure watermark resistance to global affine geometric attacks (rotation, scaling, translation and shearing) the design of the watermarking scheme is modified, mainly, by incorporating a normalization procedure. Image normalization, a means to achieve invariance to geometric transformations, is well known in computer vision and pattern recognition areas. In this new design, both watermark embedding and detection are carried out in the Hermite transform domain of moment-based normalized images. A sequence of tests is conducted on various images. Many removal attacks (.JPEG compression, additive noise and filtering) as well as geometric attacks are applied from the Checkmark benchmark. Experimental results show the effectiveness of the whole scheme in achieving Its goals in terms of watermark invisibility and robustness.