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
A novel image watermarking scheme based on support vector regression
Journal of Systems and Software
SVR-Parameters Selection for Image Watermarking
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A feature-based robust digital image watermarking scheme
IEEE Transactions on Signal 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
An optimal robust digital image watermarking based on genetic algorithms in multiwavelet domain
WSEAS Transactions on Signal Processing
Image watermarking method in multiwavelet domain based on support vector machines
Journal of Systems and Software
Embedding capacity raising in reversible data hiding based on prediction of difference expansion
Journal of Systems and Software
A color image watermarking scheme based on artificial immune recognition system
Expert Systems with Applications: An International Journal
A new robust digital watermarking algorithm based on genetic algorithms and neural networks
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
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A new digital image watermarking scheme based on Schur decomposition
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Adjustable prediction-based reversible data hiding
Digital Signal Processing
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In image watermarking area, the robustness against desynchronization attacks, such as rotation, translation, scaling, row or column removal, cropping, and local random bend, is still one of the most challenging issues. This paper presents a support vector machine (SVM)-based digital image-watermarking scheme, which is robust against a variety of common image-processing attacks and desynchronization attacks. To protect the copyright of a digital image, a signature (a watermark), which is represented by a binary image, is embedded in the digital image. The watermark embedding and watermark extraction issues can be treated as a classification problem involving binary classes. Firstly, a set of training patterns is constructed by employing two image features, which are the sum and variance of some adjacent pixels. This set of training patterns is gathered from a pair of images, an original image and its corresponding watermarked image in the spatial domain. Secondly, a quasi-optimal hyperplane (a binary classifier) can be realized by an SVM, and the SVM can be trained by utilizing the set of training patterns. Finally, the trained SVM is applied to classify a set of testing patterns. Following the results produced by the classifier (the trained SVM), the digital watermark can be embedded and retrieved. 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, cropping, and local random bend, etc.