Marzieh Amini

A new blind wavelet domain watermark detector using hidden Markov model

The wavelet coefficients of images show heavy-tailed marginal statistics as well as strong inter- and intra-subbands and across orientations dependencies. The vector-based hidden Markov model (HMM) has been shown to be an effective statistical model for wavelet coefficients, which is capable of capturing both the subband marginal distribution and the inter-scale and intra-scale dependencies of the wavelet coefficients. In this paper, we propose a locally-optimum watermark detector using the HMM model for image wavelet coefficients. The performance of the proposed detector is studied through simulation and is shown to be superior to that of other detectors in terms of the imperceptibility of the embedded watermark and detection rate.

Image denoising in wavelet domain using the vector-based hidden Markov model

Denoising problems can be regarded as that of a prior probability modeling in an estimation task. The performance of the estimator is intimately related on the correctness of the model. This paper proposes a new wavelet-domain image denoising method using the minimum mean square error (MMSE) estimator. The vector-based hidden Markov model (HMM) is used as the prior for modeling the wavelet coefficients of an image. This model is an effective statistical model for the wavelet coefficients, since it is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross-orientation dependencies of the wavelet coefficients. Using this prior, a Weiner filter, which is derived using a MMSE estimator, is developed for estimating the denoised coefficients. Experiments are conducted on standard images to evaluate the performance of the proposed method. Simulation results are provided to show that the proposed denoising method can effectively reduce the noise in yielding higher values for the peak signal-to-noise ratio along with better visual quality than that provided by some of the other existing methods.

Digital watermark extraction in wavelet domain using hidden Markov model

A watermark decoder aims at extracting the hidden watermark bits from the digital data. Statistical modeling of wavelet subband coefficients has been used in watermark extraction schemes. It is known that the effectiveness of such schemes depends on how accurately the wavelet coefficients are modeled. The vector-based hidden Markov model (HMM) is a very powerful statistical model for describing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross orientation dependencies of the wavelet coefficients. It is shown that the vector-based HMM gives a better fit for the empirical data compared to the previously-used distributions. In view of this, we propose a watermark decoder using the vector-based HMM in the wavelet domain. The watermark decoder is designed based on the maximum likelihood criterion. Closed-form theoretical expression for the watermark decoder is derived. The performance of the proposed decoder is assessed using a number of test images. It is shown that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. The proposed decoder is shown to be highly robust against various kinds of attacks.

A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain

The vector-based HMM is used to model the wavelet coefficients of images.A new watermark detector using vector-based HMM is proposed.Closed-form expression for test statistic is derived and experimentally validated.The proposed vector-based HMM detector outperforms other existing detectors.The vector-based HMM detector is highly robust against various kinds of attacks. Watermark detection is a way of verifying the existence of a watermark in a watermarking scheme used for copyright protection of digital data. Statistical modeling of wavelet subband coefficients has been extensively used in watermark detection. The effectiveness of a watermarking scheme depends directly on how the wavelet coefficients are modeled. It is known that the vector-based hidden Markov model (HMM) is a very powerful statistical model for describing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross orientation dependencies of the wavelet coefficients. In this paper, it is shown that modeling using the vector-based HMM gives a better fit for the empirical data in comparison to modeling with Cauchy, Bessel-K form (BKF) and generalized Gaussian (GG) distributions. In view of this, we propose a locally-optimum blind watermark detector using the vector-based HMM in the wavelet domain. In a Bayesian framework, closed-form expressions for the mean and variance of a test statistic are derived, experimentally validated and used in evaluating the performance of the proposed detector. Using a number of test images, the performance of the proposed detector is evaluated. It is shown that the proposed detector provides a detection rate higher than that provided by other detectors designed based on the Cauchy, Gaussian, BKF or GG distributions for the wavelet coefficients. The proposed detector is also shown to be highly robust against various kinds of attacks.

A Robust Multibit Multiplicative Watermark Decoder Using a Vector-Based Hidden Markov Model in Wavelet Domain

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.

Dual wavelet watermarking using principal component analysis

In this article, a new scheme of dual wavelet watermarking by applying principal component analysis is presented. First, we identify the two best sub bands of wavelet transform by considering the intensity variance of each sub band. These two sub bands are further transformed by 1-level wavelet transform. The embedding locations are obtained by experiment in a way that they would be compatible with the human visual system. Both watermarks are embedded within the selected sub bands with respect to their principal components. We take advantage of interleaving technique to improve imperceptibility of the algorithm. Experimental results show good robustness and security of the proposed method against various attacks such as JPEG compression, cropping, gamma correction and Gaussian filtering.

A new map estimator for wavelet domain image denoising using vector-based hidden Markov model

There are a number of image denoising methods in the wavelet domain using statistical models. It is known that the performance of such methods can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It is shown that the vector-based hidden Markov model (VB-HMM) is capable of capturing both the subband marginal distribution and the inter-scale, intra-scale and cross orientation dependencies of the wavelet coefficients. In view of this, we propose a new maximum a posteriori estimator using the VB-HMM as a prior for the wavelet coefficients of images. This is realized by deriving an efficient closed-form expression for the shrinkage function. Experimental results are performed to evaluate the performance of the proposed denoising method. The results demonstrate that the proposed method outperforms some of the state-of-the-art techniques in terms of both the peak signal to noise ratio and perceptual quality.

Binary image watermarking in ridgelet domain

Binary images have only two distinct pixel color values so the capability of data hiding is very limited. To improve the robustness of watermarking algorithm, we proposed a novel ridgelet based watermarking for binary images. Ridgelet transform is efficient for representing images with line singularities. So, binary host image is partitioned into several non-overlapping blocks to make edges in each block similar to straight edges. Ridgelet transform is applied to each single block. To embed the watermark bits, directions with highest variance are selected in ridgelet coefficients matrix. To extract the watermark logo, detector response is computed for several sample watermarks and the maximum value is chosen as the extracted watermark. The proposed method has great robustness against different kinds of attacks.

SAR image despeckling using vector-based hidden Markov model in wavelet domain

Despecking is an essential part of any synthetic aperture radar (SAR) imagery systems. In this work, we propose a new despeckling method for SAR images in the wavelet domain. The performance of a method can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It has been shown that the vector-based hidden Markov model (HMM) is capable of capturing the subband marginal distribution and the inter-scale and cross orientation dependencies of the wavelet coefficients. In view of this, in order to estimate a speckle-free SAR image, the Bayesian maximum a posteriori estimator using the vector-based HMM as a prior for the wavelet coefficients of images is developed by using the real and synthetically-speckled SAR images. The performance of the proposed despeckling method is evaluated and shown to be superior to some of the existing techniques in terms of providing better preservation of the details and yielding better visual quality.

Multichannel color image watermark detection utilizing vector-based hidden Markov model

Multimedia data piracy in the Internet is a growing problem, since it provides easy and fast data transmission. Watermarking is regarded as a solution to restrain unauthorized duplication or distribution data. Image watermarking research mostly focuses on grayscale images with an extension to color images. However, most of these techniques ignore dependencies between color channels. In view of this, in this work, a multichannel color image watermarking technique and its corresponding detector in the wavelet domain is proposed. The inter-channel dependencies between RGB channels and inter-scale dependencies of the wavelet coefficients of color image are taken into account by employing the vector-based hidden Markov model. We conduct experiment on a set of color images to assess the performance of the proposed watermark detector. The results show that the performance of the proposed detector is superior to that of the other detectors in terms of the imperceptibility of the embedded watermark and the detection rate. It is also shown that the proposed detector has better performance in presence or absence of different kinds of attacks in comparison to the other existing methods.