Semi-Fragile Zernike Moment-Based Image Watermarking for Authentication

We propose a content-based semi-fragile watermarking algorithm for image authentication. In content-based watermarking scheme for authentication, one of the most challenging issues is to define a computable feature vector that can capture the major content characteristics. We identify Zernike moments of the image to generate feature vector and demonstrate its good robustness and discriminative capability for authentication. The watermark is generated by quantizing Zernike moments magnitudes (ZMMs) of the image and embedded into DWT (Discrete Wavelet Transform) subband. It is usually hard to locate the tampered area by using global feature in the content-based watermarking scheme. We propose a structural embedding method to locate the tampered areas by using the separability of Zernike moments-based feature vector. The authentication process does not need the original feature vector. By using the semi-fragilities of the feature vector and the watermark, the proposed authentication scheme is robust to content-preserved processing, while being fragile to malicious attacks. As an application of our algorithm, we apply it on Chinese digital seals and the results show that it works well. Compared with some existing algorithms, the proposed scheme achieves better performance in discriminating high-quality JPEG compression from malicious attacks.