Robust Digital Image Watermarking Algorithm Using BPN Neural Networks

摘要

This paper proposes a new watermarking scheme in which a logo watermark is embedded into the spatial domain of the original image using Back-Propagation neural networks (BPN). BPN will learn the characteristic of the image, and then watermark is embedded and extracted by the trained BPN. The image is divided into 8x8 blocks and the average pixel value of each block is used as the desired output value of the BPN. The quantized DC coefficient of discrete cosine transform (DCT) domain of each block is used as input value of the BPN to be trained. After the BPN is trained using those input/output values, watermark is embedded into the spatial domain using the trained BPN. The trained BPN also used in watermark extracting process. Experimental results show that the proposed method has good imperceptibility and high robustness to common image processing.