Optimum Detection of Multiplicative-Multibit Watermarking for Fingerprint Images

Watermarking is an attractive technique which can be used to ensure the security and the integrity of fingerprint images. This paper addresses the problem of optimum detection of multibit, multiplicative watermarks embedded within Generalized Gaussian distribution features in Discrete Wavelet Transform of fingerprint images. The structure of the proposed detector has been derived using the maximum-likelihood approach and the Neyman-Pearson criterion. The parameters of the Generalized Gaussian distribution are directly estimated from the watermarked image, which makes the detector more suitable for real applications. The performance of the detector is tested by taking into account the different quality of fingerprint images and different attacks. The results obtained are very attractive and the watermark can be detected with low detection error. Also, the results reveal that the proposed detector is more suitable for fingerprint images with good visual quality.