WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 10, 2014
Multiobjective Image Data Hiding Based on Neural Networks and Memetic Optimization
Authors: , ,
Abstract: This paper presents a hybridization of neural networks and multiobjective memetic optimization for an adaptive, robust, and perceptual data hiding method for colour images. The multiobjective optimization problem of a robust and perceptual image data hiding is introduced. In particular, trade-off factors in designing an optimal image data hiding to maximize the quality of watermarked images and the robusteness of watermark are investigated. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for the efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of GRNN are searched by a MOMA. The experimental results show that the propsed approach achieves adaptation, robustness, and imperceptibility in image data hiding.
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Keywords: Information hiding, image data hiding, image watermarking, multiobjective optimization, memetic optimization, general regression neural networks, wavelet transforms, human visual system, quality metrics
Pages: 645-661
WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 10, 2014, Art. #67