Zero Digital Images Watermarking Method Based on Cellular Neural Network and Contourlet Transform

Abstract

Watermark embedding introduces inevitably some perceptible quality degradation of the host image. Another problem is the inherent conflict between imperceptibility and robustness. However, zero-watermarking technique can extract some essential characteristics from the host image and use them for watermark registration and detection. The original image was decomposed into series of multiscale and directional subimages after contourlet transform. The low-frequency subimage and watermark image are inputs of the cellular neural network (CNN), and the zero-watermarking registration image is the output. The geometric moments and log-polar mapping are employed to against scaling and rotation attacks. To investigate and improve the security and robustness, the original watermark and registration image are scrambled or encrypted. The proposed method is simple for hardware realization. Experimental results show that it is robust to many common image operations.