What is Hopfield Neural Network

The digital multimedia, including text, image, graphics, audio, video, and so forth, has become a main way for information communication along with the popularization of Internet and the development of multimedia techniques. How to provide copyright protection has drawn extensive attention in recent years. As a main method for copyright protection, digital watermarking has been widely studied and applied. In this chapter we discuss the important properties of watermarks, the capacity, and the detection error rate. The watermarking system is analyzed based on the channel capacity and error rate of the communication system, and the relation between the detection error rate with the capacity and payload capacity is derived. This chapter also introduces a new analysis method of the watermarking capacity, which is based on the theories of attractors and attraction basin of artificial neural network. The attraction basin of neural network decides the upper limit of watermarking, and the attractors of neural network decide the lower limit of watermarking. According to the experimental results, the detection error rate of watermark is mainly influenced by the watermark average energy and the watermarking capacity. The error rate rises with the increase of watermarking capacity. When the channel coding is used, the watermarking error rate drops with the decrease of the payload capacity of watermarking.