New detectors for watermarks with unknown power based on student-t image priors

被引：7

作者：

Mairgiotis, Antonis ^{[1
]}

Galatsanos, Nikolaos ^{[1
]}

Chantas, Giannis ^{[1
]}

Blekas, Konstantinos ^{[1
]}

Yang, Yongyi ^{[2
]}

机构：

[1] Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece

[2] IIT, Dept Elect & Comp Engn, Chicago, IL 60616 USA

来源：

2007 IEEE NINTH WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING | 2007年

关键词：

watermark detection; Bayesian inference; student-t image prior; EM algorithm; GLRT test; Rao test;

D O I：

10.1109/MMSP.2007.4412889

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present new detectors for additive watermarks when the power of the watermark is unknown. These detectors are based on modeling the image using Student-t statistics. As a result, due to the generative properties of the Student-t density function, such models are spatially adaptive and the Expectation-Maximization algorithm can be used to obtain maximum likelihood estimates of their parameters. Using these image models detectors based on the Generalized Likelihood Ratio and Rao tests are derived for this problem. Numerical experiments are presented that demonstrate the properties of these detectors and compared them with previously proposed detectors.

引用

页码：353 / +

页数：2

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