Optimal Colour Image Watermarking Using Neural Networks and Multiobjective Memetic Optimization

Authors: Hieu V. Dang, Witold Kinsner

Abstract: This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. 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 (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of the GRNN are searched by a MOMA for optimally embedding watermark bits into wavelet coefficients. The experimental results show that the proposed approach achieves robustness and imperceptibility in watermarking.

Pages: 23-32

DOI: 10.46300/91016.2022.9.5

International Journal of Neural Networks and Advanced Applications, E-ISSN: 2313-0563, Volume 9, 2022, Art. #5