A Study in Image Watermarking Schemes using Neural Networks

The digital watermarking technique, an effective way to protect image, has become the research focus on neural network. The purpose of this paper is to provide a brief study on broad theories and discuss the different types of neural networks for image watermarking. Most of the research interest image watermarking based on neural network in discrete wavelet transform or discrete cosine transform. Generally image watermarking based on neural network to solve the problem on to reduce the error, improve the rate of the learning, achieves goods imperceptibility and robustness. It will be useful for researches to implement effective image watermarking by using neural network

Comparison of multiple watermarking techniques using genetic algorithms

Multiple watermarking is used to share the copyright of multiple users, increase robustness and high security. The proposed method is comparison of multiple-watermarking techniques based on Discrete Wavelet Transform and Singular Value Decomposition using Genetic algorithm. This research elaborates the three main categories of multiple watermarking techniques such as successive, segmented and composite watermarking. The experimental results show that the DWT-based watermarking algorithms possess multi-resolution description characteristics achieving imperceptibility. The SVD-based watermarking algorithms add the watermark information to the singular values of the diagonal matrix achieving robustness requirements. The optimization is to maximize the performance of peak signal to noise ratio and normalized correlation in multiple watermarking techniques using genetic algorithms

DWT SVD BASED SEGMENTED WATERMARKING SCHEME USING GENETIC ALGORITHM

A multiple color image segmented watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is presented. In the proposed approach, the original image is segmented into two sub images, and then the two color watermarks are embedded in the singular values of each sub image separately. In the extraction process the watermarks are extracted from the singular values of the watermarked sub images. The segmentation of multiple watermarking processes makes the watermarks much more robust to the attacks such as noise, filtering, compression, rotation, cropping, translation, sharpening, smoothing, row-column blanking Intensity transformation, and row-column copying. The optimization on segmented watermarking achieves more imperceptibility and robustness