The nature of statistical learning theory
The nature of statistical learning theory
A Multibit Geometrically Robust Image Watermark Based on Zernike Moments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Geometric Attacks on Image Watermarking Systems
IEEE MultiMedia
SVR-Parameters Selection for Image Watermarking
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Digital Watermarking and Steganography
Digital Watermarking and Steganography
A feature-based robust digital image watermarking scheme
IEEE Transactions on Signal Processing
Geometrically invariant watermarking using feature points
IEEE Transactions on Image Processing
Robust image watermarking based on generalized Radon transformations
IEEE Transactions on Circuits and Systems for Video Technology
Invariant image watermark using Zernike moments
IEEE Transactions on Circuits and Systems for Video Technology
A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression
IEEE Transactions on Circuits and Systems for Video Technology
Invariant image watermarking using multi-scale Harris detector and wavelet moments
Computers and Electrical Engineering
| Hi-index | 0.02 |
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.