Maximum-Likelihood watermarking detection on fingerprint images

Khalil Zebbiche; Fouad Khelifi; Ahmed Bouridane

doi:10.1109/BLISS.2007.21

Maximum-Likelihood watermarking detection on fingerprint images

Khalil Zebbiche, Fouad Khelifi, Ahmed Bouridane

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

The integrity and security of fingerprint images can be achieved 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. The ML method, which is based on Bayes' decision theory and the Neyman-Pearson criterion, requires a probability distribution function (PDF), which must correctly model the statistical behavior of the DWT coefficients. The performance of the detector is tested by taking into account the different quality of fingerprint images. Both Generalized Gaussian (GG) and Laplacian models provide attractive results but with a slight superiority for the GG model.

Original language	English
Title of host publication	BLISS 2007
Subtitle of host publication	2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security
Publisher	IEEE
Pages	15-18
Number of pages	4
ISBN (Electronic)	9780769529196
DOIs	https://doi.org/10.1109/BLISS.2007.21
Publication status	Published - 20 Aug 2007
Externally published	Yes

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title = "Maximum-Likelihood watermarking detection on fingerprint images",

abstract = "The integrity and security of fingerprint images can be achieved 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. The ML method, which is based on Bayes' decision theory and the Neyman-Pearson criterion, requires a probability distribution function (PDF), which must correctly model the statistical behavior of the DWT coefficients. The performance of the detector is tested by taking into account the different quality of fingerprint images. Both Generalized Gaussian (GG) and Laplacian models provide attractive results but with a slight superiority for the GG model.",

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AB - The integrity and security of fingerprint images can be achieved 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. The ML method, which is based on Bayes' decision theory and the Neyman-Pearson criterion, requires a probability distribution function (PDF), which must correctly model the statistical behavior of the DWT coefficients. The performance of the detector is tested by taking into account the different quality of fingerprint images. Both Generalized Gaussian (GG) and Laplacian models provide attractive results but with a slight superiority for the GG model.

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Maximum-Likelihood watermarking detection on fingerprint images

Abstract

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