Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Geometric Attacks on Image Watermarking Systems
IEEE MultiMedia
Geometrically invariant watermarking: synchronization through circular Hough transform
Multimedia Tools and Applications
Image watermarking based on invariant regions of scale-space representation
IEEE Transactions on Signal Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Digital watermarking robust to geometric distortions
IEEE Transactions on Image Processing
Invariant image watermark using Zernike moments
IEEE Transactions on Circuits and Systems for Video Technology
A novel color image watermarking scheme in nonsampled contourlet-domain
Expert Systems with Applications: An International Journal
Geometrically invariant image watermarking using SVR correction in NSCT domain
Computers and Electrical Engineering
A novel pyramidal dual-tree directional filter bank domain color image watermarking algorithm
ICICS'11 Proceedings of the 13th international conference on Information and communications security
A new SVM-based image watermarking using Gaussian-Hermite moments
Applied Soft Computing
Journal of Visual Communication and Image Representation
Watermarking technique for wireless multimedia sensor networks: a state of the art
Proceedings of the CUBE International Information Technology Conference
A robust blind color image watermarking in quaternion Fourier transform domain
Journal of Systems and Software
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
A robust digital watermarking algorithm in undecimated discrete wavelet transform domain
Computers and Electrical Engineering
Engineering Applications of Artificial Intelligence
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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.