Zernike Moments and Edge Features Based Semi- Fragile Watermark for Image Authentication with Tampering Localization

This paper present a novel content-based image authentication framework which embeds the semi-fragile image feature into the host image based on wavelet transform. In this framework, two features of a target image from the low frequency domain to generate two watermarks: Zernike moments for classifying of the intentional content modification and Sobel edge features for indicating the modified location. In particular, we design a systematic method for automatic order selection of Zernike moments and in order to tell if the processing on the image is malicious or not. We also propose a weighted Euclidean distance by a reconstruction process. An important advantage of our approach is that it can tolerate compression and noise to a certain extent while rejecting common attack of the image like rotation. Experimental results show that the framework can locate the malicious tampering for the resolution of a 8x8 block. Also, it is robust to content preserved processing, such as JPEG compression Q≧30 and Gaussian noise variance ≦20.