Antonis Mairgiotis

New Additive Watermark Detectors Based On A 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 hierarchical, two-level 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, which include a generalized likelihood ratio, Bayesian, and Rao test detectors. We also propose methods to estimate the necessary parameters for these detectors. Our numerical experiments demonstrate that these new detectors can lead to superior performance to several state-of-the-art detectors.

New Detectors for Watermarks with Unknown Power Based on Student-t Image Priors

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.

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.

Bayesian watermark detection and new perceptual mask based on a spatially weighted Total Variation image prior

In this work we propose a class of bayesian watermark detectors based on a spatially weighted Total Variation (TV) image model. The inherent flexibility of the proposed prior in modelling local image variations, provides us with a novel spatial mask capable to perceptually shape the embedded watermark. We also propose methods to estimate the parameters of the proposed mask, creating watermarks with more energy and in consequence with improved robust properties. Numerical experiments are presented that demonstrate the performance of our proposal with regard to detection sensitivity and the superiority of the mask compared with other existing spatial masking schemes.

Locally optimum detection for additive watermarking in the DCT and DWT domains through non-Gaussian distributions

This work presents a new locally optimal blind detector for the additive transform-based image watermarking problem. Working in non-Gaussian environments, we introduce a new statistical model and its consequent application in the design of a locally optimum detection test. More specifically, we model the marginal distributions of the detail subband coefficients of DWT (Discrete Wavelet Transform) or DCT (Discrete Cosine Transform) with Student-t distribution. Since the watermark signal has low power, locally most powerful (LMP) detector is a valid choice. The experimental results show that the proposed detector has superior performance than alternative LMP detectors based on known state of the art statistical models.

DWT-based additive image watermarking using the Student-t prior

In this work, a class of new blind watermark detectors is proposed for the DWT (Discrete Wavelet Transform)-based additive image watermarking problem. More specific, we model the marginal subband wavelet distributions with the Student-t probability density function (pdf) deriving a new watermark detector. The proposed detector shows high performance with regard to the watermark detection and increased robust properties against intentional or unintentional attacks. Experimental results on real images demonstrate these properties comparing the proposed detector with other state of the art methods in the transform domain.

A hierarchical spatially adaptive image model for perceptual mask design and multiplicative watermark detection

In this work motivated by a hierarchical spatially adaptive image prior that we have developed for additive watermarking; we first, propose a new perceptual mask which improves robustness of additive watermark detectors in the spatial domain. The proposed mask is based on the local image variations along the two principal directions and enhances the watermarks energy while satisfying the imperceptibility requirement. Second we extent the application of the proposed hierarchical image prior for the multiplicative watermarking problem and we propose new watermark detectors. Numerical results are provided that demonstrate both the value of the proposed mask, and the improved sensitivity as compared to additive watermarking, with the same image model, of the proposed multiplicative watermark detector. Furthermore, we demonstrate its improved robustness compared to other state of the art similar in spirit watermark detectors.

Rao based watermark detector through two-level hierarchical prior in transform domain

In this work, we propose a transform based blind zero-bit watermark detector, designed based on a hierarchical, two-level image prior. This model is applied through Rao hypothesis test, where we detect the hidden information with unknown amplitude. The proposed method is suitable for wavelet domain watermark detection without the need to estimate the appropriate parameters under the alternative hypothesis. The proposed system shows efficient robustness against a variety of known attacks. The experimental results show that the proposed detector has a good performance with or without attacks in terms of ROC (receiver operating characteristics) curves compared with other known state-of-the-art statistical detectors.