The nature of statistical learning theory
The nature of statistical learning theory
SVR-based oblivious watermarking scheme
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
An audio watermarking scheme with neural network
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
DCT-based image watermarking using subsampling
IEEE Transactions on Multimedia
A learning-based audio watermarking scheme using kernel Fisher discriminant analysis
Digital Signal Processing
Audio watermarking scheme robust against desynchronization attacks based on kernel clustering
Multimedia Tools and Applications
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How to protect the copyright of digital media over the Internet is a problem for the creator/owner. A novel support vector regression (SVR) based digital audio watermarking scheme in the wavelet domain which using subsampling is proposed in this paper. The audio signal is subsampled and all the sub-audios are decomposed into the wavelet domain respectively. Then the watermark information is embedded into the low-frequency region of random one sub-audio. With the high correlation among the sub-audios, accordingly, the distributing rule of different sub-audios in the wavelet domain is similar to each other, SVR can be used to learn the characteristics of them. Using the information of unmodified template positions in the low-frequency region of the wavelet domain, the SVR can be trained well. Thanks to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks. The proposed watermarking method which doesn't require the use of the original audio signal for watermark extraction can provide a good copyright protection scheme. The experimental results show the algorithm is robust to signal processing, such as lossy compression (MP3), filtering, resampling and requantizing, etc.