A Novel Image Watermarking Scheme Based on MCA Neural Networks
Yanmei Li, Jian Jun Luo · Applied Mechanics and Materials · 2012
Minor component analysis (MCA) plays an important role in signal processing and data analysis. In this paper, we propose a novel watermarking scheme using MCA neural networks. The proposed scheme can detect and localize any modification made to the image, which can be used for integrity verification and authentication. Another important property of this proposed scheme is that any gray scale watermark can be easily inserted into the image compared to some other watermarking method such as LSB (least significant bit) method. Furthermore, the proposed scheme can insert any watermark into one image without making any visible changes to the image. Since the minor component of each image is different, and it requires a user key during both the insertion and extraction procedures, it is not possible for an infringer to insert a new watermark or alter the existing one that could pass the test. Finally, various experiments also confirm such properties of the proposed scheme.