Multi-objective genetic algorithm optimization for image watermarking based on singular value decomposition and lifting wavelet transform

Abstract

In this paper, a new optimal watermarking scheme based on singular value decomposition (SVD) and lifting wavelet transform (LWT) using multi-objective genetic algorithm optimization (MOGAO) is presented. The singular values of the watermark is embedded in a detail subband of host image. To achieve the highest possible robustness without losing watermark transparency, multiple scaling factors (MSF) are used instead of single scaling factor (SSF). Determining the optimal values of the MSFs is a difficult problem. However, to find this values a multi-objective genetic algorithm optimization is used. Experimental results show a much improved performance in term of transparency and robustness of the proposed method compared to others methods. © Springer-Verlag Berlin Heidelberg 2010.