The nature of statistical learning theory
The nature of statistical learning theory
A DCT-domain system for robust image watermarking
Signal Processing
Robust image watermarking in the spatial domain
Signal Processing
Digital watermarking based on neural networks for color images
Signal Processing - Special section on digital signal processing for multimedia communications and services
Image and Video Compression for Multimedia Engineering
Image and Video Compression for Multimedia Engineering
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Image refining technique using digital watermarking
IEEE Transactions on Consumer Electronics
Image-adaptive watermarking using visual models
IEEE Journal on Selected Areas in Communications
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Robust template matching for affine resistant image watermarks
IEEE Transactions on Image Processing
Reliable information bit hiding
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a reliable information hiding scheme based on support vector machine and error correcting codes is proposed. To extract the hidden information bits from a possibly tampered watermarked image with a lower error probability, information hiding is modeled as a digital communication problem, and both the good generalization ability of support vector machine and the error correction code BCH are applied. Due to the good learning ability of support vector machine, it can learn the relationship between the hidden information and corresponding watermarked image; when the watermarked image is attacked by some intentional or unintentional attacks, the trained support vector machine can recover the right hidden information bits. The reliability of the proposed scheme has been tested under different attacks. The experimental results show that the embedded information bits are perceptually transparent and can successfully resist common image processing, jitter attack, and geometrical distortions. When the host image is heavily distorted, the hidden information can also be extracted recognizably, while most of existing methods are defeated. We expect this approach provide an alternative way for reliable information hiding by applying machine learning technologies.