Maximum-Likelihood Based Watermark Detection on Fingerprint Images
نویسندگان
چکیده
The integrity and security of fingerprint images can be achieved by using watermarking techniques. We introduce Maximum-Likelihood (ML) watermark detection method to detect an invisible watermark within discrete wavelet transform (DWT) coefficients of fingerprint images. Such an optimum technique, which is based on Bayes’ decision theory and the Neyman-Pearson criterion, requires parameters of a probability distribution function (PDF) which must model the statistical behavour of the DWT coefficients accurately. Both Generalized Gaussian (GG) and Laplacian models yield attractive results but with a slight superiority for the GG model.
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