Image Watermarking Detection Based on SVR Geometric Correction
一种基于SVR几何校正的数字水印检测算法

In this paper, a robust image watermarking detection based on support vector regression (SVR) is proposed. Firstly, six combined low order image moments are taken as the feature vector and the geometric transformation parameters are regarded as the training objective, the appropriate kernel function is selected for the training, and a SVR training model can be obtained. Secondly, the combined moments for test image are selected as input vector, the actual output 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. Finally, 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 signals processing such as filtering, sharpening, noise adding, JPEG compression etc, but also robust against the geometric attacks such as rotation, translation, scaling, cropping, combination attacks, etc.