Image watermarking capacity analysis using hopfield neural network

Abstract

Image watermarking capacity research is to study how much information can be hidden in an image. In watermarking schemes, watermarking can be viewed as a form of communication and image can be considered as a communication channel to transmit messages. Almost all previous works on watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. This paper presents a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. Result shows that the attraction basin of associative memory decides watermarking capacity. © Springer-Verlag 2004.