Unsupervised classification with non-Gaussian mixture models using ICA
Proceedings of the 1998 conference on Advances in neural information processing systems II
ICA for watermarking digital images
The Journal of Machine Learning Research
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
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
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The authors propose a new solution to the blind robust watermarking of digital images. In this approach we embed the watermark into the independent components of the image. Since independent components are related to the edges of the image, this method has a little perceptual impact on the watermarked image. Besides, we exploit the orthogonality of independent components and spread-spectrum generated watermarks in the blind extraction of the watermark. As extraction algorithm we use a simple matched filter. We also improve this novel method with standard techniques such as perceptual masking and holographic properties. Some experiments are included to illustrate the good performance of the algorithm against compression, cropping, filtering or quantization based attacks.