Application of reversible discrete-time cellular neural networks to image copyright labelling

In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary random image sequences. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Due to the reversibility of a reversible DTCNN, the same reversible DTCNN is used to recover the copyright label from a labelled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. Computer simulation results are presented.