Ten lectures on wavelets
A DCT-domain system for robust image watermarking
Signal Processing
High-order contrasts for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
A Stochastic Approach to Content Adaptive Digital Image Watermarking
IH '99 Proceedings of the Third International Workshop on Information Hiding
ICA for watermarking digital images
The Journal of Machine Learning Research
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
Image-adaptive watermarking using visual models
IEEE Journal on Selected Areas in Communications
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Improved wavelet-based watermarking through pixel-wise masking
IEEE Transactions on Image Processing
Modified patchwork-based watermarking scheme for satellite imagery
Signal Processing
Feature based RDWT watermarking for multimodal biometric system
Image and Vision Computing
Curvelet-Domain Image Watermarking Based on Edge-Embedding
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Rubik's cube watermark technology for grayscale images
Expert Systems with Applications: An International Journal
The RST invariant digital image watermarking using Radon transforms and complex moments
Digital Signal Processing
A robust watermarking technique for copyright protection using discrete wavelet transform
WSEAS Transactions on Computers
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This paper proposes a new approach to watermarking multimedia products by redundant discrete wavelet transform (RDWT) and independent component analysis (ICA). For watermark security, embedded logo watermarks are encrypted to random noise signal. To embed logo watermarks, the original image is decomposed by RDWT, and watermarks are embedded into middle frequency at LH and HL sub-bands. The perceptual model is applied with a stochastic multiresolution model for adaptive watermark embedding. This is based on computation of a noise visibility function (NVF) which has local image properties. We also propose an intelligent ICA-based detector which directly extracts watermarks in spatial domain. A novel characteristic of this detection is that it does not require the transformation process to extract the watermark. The experimental results show that logo watermarks are extracted perfectly, and also demonstrate the robustness of the method.