Additive Watermark Detectors Based on a New Hierarchical Spatially Adaptive Image Model

In this paper we propose a new family of watermark detectors for additive watermarks in digital images. These detectors are based on a recently proposed two-level, hierarchical image model, which was found to be beneficial for image recovery problems. The top level of this model is defined to exploit the spatially-varying local statistics of the image, while the bottom level is used to characterize the image variations along two principal directions. Based on this model we derive a class of detectors for the additive watermark detection problem, including the generalized likelihood ratio test (GLRT) and Rao detectors.

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