Abstract
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 Zernikemoments 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.
Highlights
With the development of advanced image editing software, it has become easier to modify or forge digital image [1]
We propose a structural embedding method to solve this problem by using the separability of Zernike moments feature vector, which can be separated into individual moments
We demonstrate the results of locating the tampered areas when the image is processed by combining malicious manipulation with JPEG compression, sharpening, or blurring
Summary
With the development of advanced image editing software, it has become easier to modify or forge digital image [1]. In [16], the robust signature is cryptographically generated on the basis of invariant features called significancelinked connected component extracted from the image and signed and embedded into the wavelet domain as a watermark using the quantization-based method. In content-based watermarking scheme for image authentication, in order to locate the tampered areas, local feature is usually computed and embedded locally, just like the algorithms in [13, 15, 16, 19,20,21,22]. We propose to use Zernike moments to generate feature vector By using this global feature, we can decide whether the image is maliciously manipulated or not and locate the tampered areas. We identify Zernike moments to generate feature vector and demonstrate its good semi-fragile and discriminative capability for authentication.
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