A novel image watermarking scheme against desynchronization attacks by SVR revision

Abstract

In digital image watermarking, the watermark’s vulnerability to desynchronization attacks has long been a difficult problem. On the basis of support vector regression (SVR) theory and local image characteristics, a novel image watermarking scheme against desynchronization attacks by SVR revision is proposed in this paper. First, some pixels are randomly selected and the sum and variance of their neighboring pixels are calculated; second, the sum and variance are regarded as the training features and the pixel values as the training objective; third, the appropriate kernel function is chosen and trained, a SVR training model will be obtained. Finally, the sum and variance of all pixels’ neighboring pixels are selected as input vectors, the actual output can be obtained by using the well-trained SVR, and the digital watermark can be recovered by judging the output vector. Experimental results show that the proposed scheme is invisible and robust against common signals processing such as median filtering, sharpening, noise adding, and JPEG compression, etc., and robust against desynchronization attacks such as rotation, translation, scaling, row or column removal, shearing, local random bend, etc.