Digital watermark extraction in wavelet domain using hidden Markov model

Amini, Marzieh; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1007/s11042-016-3975-0

Cited by 29 publications

(6 citation statements)

References 49 publications

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“…The strategy is robust and imperceptible for different attacks. Amini et al [35] proposed a blind watermark decoder utilizing a vectorbased Hidden Markov model (HMM) in the DWT. The results demonstrated that the strategy is profoundly robust for different assaults including checkmark and offered a lower bit error rate than other methods [36].…”

Section: Related Workmentioning

confidence: 99%

Analysis of watermarking techniques in multimedia communications

Balakrishnan¹,

Syambabu²

2021

Serb J Electr Eng

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Multimedia information is critical of examining, information perceived and which are illustrated by the human cerebrum. In our brain, 33% of the cortical area concentrates only on visual information processing. Digital watermarking technology is being received to guarantee and encourage such kinds of digital data authentication, security, and copyright. These algorithms permit the expandable values of different techniques to prevent the problems of copyright issues during the transmission. This paper discusses the detailed about the point by point investigation of watermarking definition and different watermarking applications and strategies used to improve information security.

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Section: Related Workmentioning

confidence: 99%

Analysis of watermarking techniques in multimedia communications

Balakrishnan¹,

Syambabu²

2021

Serb J Electr Eng

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“…However, this watermarking scheme is not so ideal when dealing with large-capacity watermarks. Amini et al [8] used vector-based HMM to capture multiple correlations of wavelet coefficients. The statistical watermark decoder was developed by the maximum likelihood criterion.…”

Section: Related Workmentioning

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%

“…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], [10], [12], [14], [15], [16], [20], [21]. Despite the tremendous benefits, most existing transform domain multiplicative watermarking methods based on statistical modeling generally suffer from four disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Frequency domain watermarking first involves transforming the host image, commonly including matrix decomposition, three major transformations (namely discrete wavelet transform (DWT) [ 21 , 22 , 23 , 24 , 25 ], discrete cosine transform (DCT) [ 26 , 27 , 28 , 29 , 30 ], and discrete Fourier transform (DFT) [ 31 , 32 , 33 , 34 ]), and other transformations [ 20 , 35 , 36 , 37 , 38 , 39 ], followed by modifying the transform coefficients to embed the watermark.…”

Section: Introductionmentioning

confidence: 99%

Covert Communication through Robust Fragment Hiding in a Large Number of Images

Wang,

Zhong,

Feng

et al. 2024

Sensors

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For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored.

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Digital watermark extraction in wavelet domain using hidden Markov model

Cited by 29 publications

References 49 publications

Analysis of watermarking techniques in multimedia communications

Analysis of watermarking techniques in multimedia communications

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

Covert Communication through Robust Fragment Hiding in a Large Number of Images

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