This paper proposes a novel perceptual watermarking scheme operating in a Hermite transform domain. To achieve an
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
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.