A novel robust scaling image watermarking scheme based on Gaussian Mixture Model

Amirmazlaghani, Maryam; Rezghi, Mansoor; Amindavar, Hamidreza

doi:10.1016/j.eswa.2014.10.015

Cited by 43 publications

(17 citation statements)

References 32 publications

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“…Watermarking methods can be categorized into perceptible (visible) and imperceptible (invisible) techniques. However, the main focus is on imperceptible watermarking and in most references imperceptibility has been noticed as one major requirement of watermarking . Also, due to the application, digital watermarking can be covert or overt.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, the main focus is on imperceptible watermarking and in most references imperceptibility has been noticed as one major requirement of watermarking. 29 Also, due to the application, digital watermarking can be covert or overt. In covert watermarking, the existence of the watermark is hidden while in the overt watermarking the existence of the watermark is known.…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Preventing phishing attacks using text and image watermarking

Hajiali

Amirmazlaghani

Kordestani

2018

Concurrency and Computation

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Summary Phishing is a form of identity theft used to illegally gather personal and financial information by providing fake web pages designed to mimic the website of a legitimate business. In this paper, we propose a novel anti‐phishing approach based on using imperceptible watermarking and monitoring the HTTP requests. In the server‐side, a URL dependent watermark is generated and embedded in the elements of the web page, which are logo image and HTML/CSS files. In the client side, to authenticate the website and check its safety, the watermark is regenerated and compared with the extracted watermarks from the elements of the webpage. Security analysis of the proposed method demonstrates its robustness against some common attacks. The proposed method performs fully automatically and without user interaction. It can be simply implemented and used for all kinds of websites, either secured or not secured with SSL protocol.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Preventing phishing attacks using text and image watermarking

Hajiali

Amirmazlaghani

Kordestani

2018

Concurrency and Computation

Self Cite

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“…In the UDTCWT magnitude domain, when the hidden state S i of the vector x i is equal to m, λ 1 i,m and γ 1 i,m respectively represent the scale parameters and shape parameters in the first direction, λ 2 i,m and γ 2 i,m respectively represent the scale parameters and shape parameters in the second direction, λ 3 i,m and γ 3 i,m respectively represent the scale parameters and shape parameters in the third direction. In order to express the shape and scale parameters conveniently, we use…”

Section: B Weibull Mixture-based Vector Hmt and Parameter Estimationmentioning

confidence: 99%

“…So far, the commonly used transforms include discrete Wavelet transform (DWT) [1]- [8], Contourlet transform [9]- [16], dual tree complex Wavelet transform (DT-CWT) [17], nonsubsampled Shearlet transform (NSST) [18], nonsubsampled Contourlet transform (NSCT) [19], discrete Cosine transform (DCT) [20], and discrete Shearlet transform (DST) [21]. The usually adopted statistical models include Gaussian distribution [5], Gaussian mixture model (GMM) [3], bivariate Gaussian distribution [15], general Gaussian distribution (GGD) [7], [16], normal inverse Gaussian distribution (NIG) [9], [12], Bessel-K form (BKF) distribution [10], [18], Gamma distributions [17], Rayleigh distributions [17], Weibull distributions [17], [20], two dimensional generalized autoregressive conditional heteroscedasticity (2D-GARCH) model [2], [13], t Location-Scale distribution [11], [14], Cauchy distributions [19], vector-based Gaussian HMT model [6], [8] and Laplacian distribution [21]. The two most common methods of digital watermark embedding are addition [2], [4], [7], [8], [11], [13], [17], [19] and multiplication [1], [6], [9],…”

Section: Introductionmentioning

confidence: 99%

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

et al. 2020

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As one promising solution, digital watermarking has been proposed to resolve image copyright protection and content authentication, and has been applied successfully in many fields. Owing to their excellent description capability and invariance property, statistical models have become a popular tool for the image watermarking resulting in favorable trade-offs among imperceptibility, robustness and data payload. By modeling the robust undecimated dual tree complex Wavelet transform (UDTCWT) coefficient magnitudes with the Weibull mixtures based vector hidden Markov trees (HMT) and employing maximum likelihood (ML) test criterion, we propose a new image watermarking approach in UDTCWT domain in this paper. Our image watermarking approach consists of two parts, namely, embedding and extracting. In the embedding process, we compute the robust UDTCWT coefficient magnitudes with UDTCWT domain real/imaginary parts, and insert the digital watermark into the significant UDTCWT coefficient magnitude subband. In the extracting phase, robust UDTCWT coefficient magnitudes are firstly modeled by employing the Weibull mixture-based vector HMT, where the statistical properties of UDTCWT magnitudes are captured accurately. Then the expectation/conditional maximization (ECM) approach is introduced to estimate the statistical model parameters. Finally, an image watermark decoder for multiplicative watermarking is developed using the Weibull mixtures based vector HMT and ML test. The experiments show that the proposed method not only improves the imperceptibility, but also increases the robustness performance and outperforms state-of-the-art methods on a set of standard test images.

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“…Besides watermark embedding objects, it also includes statistical model establishment, model parameter estimation and detector construction methods. Some statistical models are often used, mainly including the Bessel K Form (BKF) distribution [4] [12], t location-scale distribution [11][16], generalized Gaussian (GG) distribution [10][14], Cauchy distribution [4] [19], Gaussian mixture model (GMM) [1], Laplacian distribution [9], normal inverse Gaussian (NIG) distribution [5] and Weibull distributions [20] [22]. To consider the correlation between coefficients more fully, multivariate Cauchy distribution [15], multivariate generalized Gaussian (MVGG) model [13] and Hidden Markov Model (HMM) [7][24] were proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Image Watermark Detector in RHFMs Domain using Beta Exponential Distribution

Niu

Tian

et al. 2021

Preprint

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The detection of watermarks can be achieved by statistical approaches. How to select robust modeling object, appropriate statistical model, and decision rules is one of the major issues in statistical image watermark detection. In this paper, we propose a new image watermark detector in robust fast radial harmonic Fourier moments (FRHFMs) magnitudes domain, wherein the Beta exponential distribution model and locally most powerful (LMP) decision rule are used. We first investigate the statistical modeling of the robust FRHFMs magnitudes by the Beta exponential distribution. It is shown that the Beta exponential distribution model fits the empirical data more accurately than the formerly employed statistical distributions, such as the Cauchy, Weibull, BKF and Exponential, do. Motivated by the statistical modeling results, we design a blind image watermark detector in FRHFMs magnitudes domain by using Beta exponential distribution and LMP test. Also, we utilize the Beta-exponential model to derive the closed-form expressions for the watermark detector. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed image watermark detector.

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A novel robust scaling image watermarking scheme based on Gaussian Mixture Model

Cited by 43 publications

References 32 publications

Preventing phishing attacks using text and image watermarking

Preventing phishing attacks using text and image watermarking

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

A New Statistical Image Watermark Detector in RHFMs Domain using Beta Exponential Distribution

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