A robust image watermarking algorithm using SVR detection

Geometric distortion is known as one of the most difficult attacks to resist. Geometric distortion desyn-chronizes 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 quantiz-ing 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, Due to the growth of the network (especially Internet) and mul-timedia technology, digital multimedia is widely used and ac-cessed everywhere. However, digital multimedia can be copied, manipulated, and reproduced illegally, without quality degradation and protection. Therefore, copyright protection has become a social issue. Digital watermarking has drawn a lot of attention in the past decade because it is recognized as a potential solution to a number of issues related to multimedia works, such as authorship identification, content authentication, fingerprinting, and secondary data delivery (Barni, Cox, & Kalker, 2005). With the development of watermarking technologies, attacks against watermarking systems have become more sophisticated. In general, the attacks on watermarking systems can be categorized into common signal processing and geometric distortions. While the common signal processing, such as lossy compression, sharpening, noise addition, lowpass filtering, etc., reduces watermark energy, geometric distortions induce synchronization errors between the original and the extracted watermark pattern and therefore can mislead the watermark detector. Most of the previous methods have shown robustness against …