A Study of using L1-norm with Image Watermarking on SVD Domain

This paper presents a study of image watermarking using robust singular value decomposition in L1-norm sub-space. The watermarked image attacked by noise is greatly degraded. This results in the effects of transparency and robustness of the watermarked image. Although the watermarking in SVD domain is sensitive to noise and outliers, incorporating L1-norm regression to the watermarking algorithm can help handling the missing data caused by noise and help increasing the robustness of the proposed algorithm. Experimental results show that the proposed algorithm can not only excellently reduce the bit error rates of the recovered watermark but also retain the transparency property of the watermarked image.