Local distortion resistant image watermarking relying on salient feature extraction

The purpose of this article is to present a novel method for region based image watermarking that can tolerate local image distortions to a substantially greater extent than existing methods. The first stage of the method relies on computing a normalized version of the original image using image moments. The next step is to extract a set of feature points that will act as centers of the watermark embedding areas. Four different existing feature extraction techniques are tested: Radial Symmetry Transform (RST), scale-invariant feature transform (SIFT), speeded up robust features (SURF) and features from accelerated segment test (FAST). Instead of embedding the watermark in the DCT domain of the normalized image, we follow the equivalent procedure of first performing the inverse DCT of the original watermark, inversely normalizing it and finally embedding it in the original image. This is done in order to minimize image distortion imposed by inversely normalizing the normalized image to obtain the original. The detection process consists of normalizing the input image and extracting the feature points of the normalized image, after which a correlation detector is employed to detect the possibly inserted watermark in the normalized image. Experimental results demonstrate the relative performance of the four different feature extraction techniques under both geometrical and signal processing operations, as well as the overall superiority of the method against two state-of-the-art techniques that are quite robust as far as local image distortions are concerned.