Binary Image Authentication using Zernike Moments

In this paper, we propose a content-based binary image authentication scheme. At first, we use Zernike moments magnitudes (ZMM) to generate the feature vector and demonstrate that this feature vector can represent the binary image and decide its authenticity effectively. Then the watermark is generated by quantizing ZMMs and embedded into the image. The authentication doesn't need the original watermark. The decision depends on the distance between the extracted watermark and the feature vector of the test image and a metric measure. To decrease the influence of watermarking on the feature vector, we split the binary image into two parts by a random mask, one for generating feature vector and the other for embedding watermark. Zernike moments are usually computationally expensive, so we propose a fast algorithm. Extensive experiments show that our scheme can detect malicious attacks effectively.