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.