Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A DCT-domain system for robust image watermarking
Signal Processing
Unsupervised classification with non-Gaussian mixture models using ICA
Proceedings of the 1998 conference on Advances in neural information processing systems II
Multiuser Detection
A new concept for separability problems in blind source separation
Neural Computation
ICA for watermarking digital images
The Journal of Machine Learning Research
Copyright protection of images using robust digital signatures
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Independent component analysis applied to digital image watermarking
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Application of Independent Component Analysis to Edge Detection and Watermarking
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
A new blind watermarking technique based on independent component analysis
IWDW'02 Proceedings of the 1st international conference on Digital watermarking
A sinusoidal contrast function for the blind separation of statistically independent sources
IEEE Transactions on Signal Processing
Hybrid higher-order statistics learning in multiuser detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
The RST invariant digital image watermarking using Radon transforms and complex moments
Digital Signal Processing
A blind robust watermarking scheme based on ICA and image dividing blocks
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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We propose a new method for the blind robust watermarking of digital images based on independent component analysis (ICA). We apply ICA to compute some statistically independent transform coefficients where we embed the watermark. The main advantages of this approach are twofold. On the one hand, each user can define its own ICA-based transformation. These transformations behave as ''private-keys'' of the method. On the other hand, we will show that some of these transform coefficients have white noise-like spectral properties. We develop an orthogonal watermark to blindly detect it with a simple matched filter. We also address some relevant issues as the perceptual masking of the watermark and the estimation of the detection probability. Finally, some experiments have been included to illustrate the robustness of the method to common attacks and to compare its performance to other transform domain watermarking algorithms.