A DCT-domain system for robust image watermarking
Signal Processing
Robust image watermarking in the spatial domain
Signal Processing
Digital watermarking
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Watermarking in the DCT Domain
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Watermarking Systems Engineering (Signal Processing and Communications, 21)
Watermarking Systems Engineering (Signal Processing and Communications, 21)
International Journal of Business Intelligence and Data Mining
Machine learning based adaptive watermark decoding in view of anticipated attack
Pattern Recognition
Watermarking is not cryptography
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
An adaptive digital image watermarking technique for copyright protection
IEEE Transactions on Consumer Electronics
Image-adaptive watermarking using visual models
IEEE Journal on Selected Areas in Communications
A wavelet-based watermarking algorithm for ownership verification of digital images
IEEE Transactions on Image Processing
A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression
IEEE Transactions on Circuits and Systems for Video Technology
Composite chaos-based lossless image authentication and tamper localization
Multimedia Tools and Applications
Hi-index | 0.00 |
Transform techniques generally are more robust than spatial techniques for watermark embedding. In this paper, a color image watermarking algorithm based on fractal and neural networks in Discrete Cosine Transform (DCT) domain is proposed. We apply fractal image coding technique to obtain the characteristic data of a gray-level image watermark signal and encrypt the characteristic data by a symmetric encryption before they are embedded. We then use neural networks and Human Visual System (HVS) to embed the watermark in the DCT domain. A Just Noticeable Difference (JND) threshold controller is designed to ensure the strength of the embedded data adapting to the host image itself entirely. Aiming at misjudging problem of the extracting process, maximum membership principle criterion is selected for identifying the watermark. And the CIELab color space is chosen to guarantee the stability of the results. The simulation results show that the algorithm is robust for common digital image processing methods as attacks and that the quality of the image is retained.