A robust image watermarking algorithm using SVR detection

Geometric distortion is known as one of the most difficult attacks to resist. Geometric distortion desynchronizes the location of the watermark and hence causes incorrect watermark detection. According to the Support Vector Regression (SVR), a new image watermarking detection algorithm against geometric attacks is proposed in this paper, in which the steady Pseudo-Zernike moments and Krawtchouk moments are utilized. The host image is firstly transformed from rectangular coordinates to polar coordinates, and the Pseudo-Zernike moments of the host image are computed. Then some low-order Pseudo-Zernike moments are selected, and the digital watermark is embedded into the cover image by quantizing the magnitudes of the selected Pseudo-Zernike moments. The main steps of watermark detecting procedure include: (i) some low-order Krawtchouk moments of the image are calculated, which are taken as the eigenvectors; (ii) the geometric transformation parameters are regarded as the training objective, the appropriate kernel function is selected for training, and a SVR training model can be obtained; (iii) the Krawtchouk moments of test image are selected as input vector, the actual output (geometric transformation parameters) is predicted by using the well trained SVR, and the geometric correction is performed on the test image by using the obtained geometric transformation parameters; (iv) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed watermarking detection algorithm is not only robust against common signal processing such as filtering, sharpening, noise adding, and JPEG compression etc., but also robust against the geometric attacks such as rotation, translation, scaling, cropping and combination attacks, etc.