Perceptual watermarking for Discrete Shearlet transform

Ahmaderaghi, Baharak; Rincón, Jesús Martínez del; Kurugöllü, Fatih; Bouridane, Ahmed

doi:10.1109/euvip.2014.7018398

Cited by 6 publications

(17 citation statements)

References 14 publications

(18 reference statements)

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“…Finally, a maximum likelihood detection scheme based on Laplacian modelling of the DST coefficients is implemented under a hypothesis condition using detection rules based on the Neyman-Pearson criterion in order to improve the robustness as well as adapting the watermark strength to the host image by considering the visual sensitivity. The proposed method is less sensitive to fine parameter tuning in comparison with nonblind methods [33], i.e. parameters can remain unchanged even under different attacks and the original image is not required during the detection stage.…”

Section: ) Robustnessmentioning

confidence: 99%

“…Non blind watermarking systems, such as the one proposed in [32], are limited in their application field, since they require access to the host image during the detection process. However, this is not always the case for some applications such as image authentication [4].…”

Section: A Digital Image Statistical Watermark Detection Based On DImentioning

confidence: 99%

“…In our earlier works, we already proposed DST as the transform domain for a non-blind watermarking framework based on spread spectrum [32] and a further refinement of it using perceptual models based on the human visual system [33]. While these earlier works showed the potential of DST for watermarking, they were both limited for their non-blind nature, requiring the original image during extraction.…”

Section: Introductionmentioning

confidence: 99%

“…The only exceptions are its use in our conference papers [32] for a basic non-blind watermarking framework as part of our preliminary work and conference papers [33] for a basic non-blind perceptual watermarking model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Ahmaderaghi

Kurugöllü

Rincón

et al. 2018

IEEE Trans. Comput. Imaging

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Section: ) Robustnessmentioning

confidence: 99%

Section: A Digital Image Statistical Watermark Detection Based On DImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Ahmaderaghi

Kurugöllü

Rincón

et al. 2018

IEEE Trans. Comput. Imaging

Self Cite

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“…Shearlet, a multiresolution method, provides a tremendously effective representation of images containing edges [13,14] and is widely used in watermarking algorithms [15][16][17][18]. Spread spectrum image watermarking in the discrete shearlet domain [15] employs the multiresolution 2 Mathematical Problems in Engineering representation characteristics of the DST to facilitate texture modeling for a given image. Zhao et al [16] proposed a watermarking scheme based on the extended shearlet, where the watermark was embedded into the subband with the largest information entropy.…”

Section: Introductionmentioning

confidence: 99%

Texture Directionality‐Based Digital Watermarking in Nonsubsample Shearlet Domain

Jian

Fan

Jia

et al. 2017

Mathematical Problems in Engineering

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Digital watermarking is a technique used to protect an author's copyright and has become widespread due to the rapid development of multimedia technologies. In this paper, a novel watermarking algorithm using the nonsubsample shearlet transform is proposed, which combines the directional edge features of an image. A shearlet provides an optimal multiresolution and multidirectional representation of an image based on distributed discontinuities such as edges, which ensures that the embedded watermark does not blur the image. In the proposed algorithm, the nonsubsample shearlet transform is used to decompose the cover image into directional subbands, where different directional subbands represent different directional and textured features. The subband whose texture directionality is strongest is selected to carry the watermark and is thus suitable for the human visual system. Next, singular value decomposition is performed on the selected subband image. Finally, the watermark is embedded in the singular value matrix, which is beneficial for the watermarking robustness and invisibility. In comparison with related watermarking algorithms based on discrete wavelet transforms and nonsubsample contourlet transform domains, experimental results demonstrate that the proposed scheme is highly robust against scaling, cropping, and compression.

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Application of discrete transforms with selective coefficients for blind image watermarking

Lydia

Raj

Selvam³

et al. 2019

Trans Emerging Tel Tech

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Watermarking scheme is not helpful in conveying the original image continually so as identifying the owner's mark from the watermarked image. In the current situation, our chosen topic is significant in real‐time applications like fraud recognition, emergency clinic, and government divisions. In this study, an optimal multiblind watermarking model is proposed for the watermark detection process. Our proposed model is a combination of intelligent domain transforms like Discrete Shearlet Transform and Discrete Curvelet Transform (DCurT). The imperceptibility necessity of the plan is accomplished utilizing optimal coefficients that are performed by applying in DCurT with metaheuristic optimization model that is Grasshopper Algorithm. It is played by a chasing behavior of gathering of grasshoppers and cooperation process, here, Random Grasshopper Optimization is utilized for watermarking. The secret image is embedded with a lower band of optimal coefficients with DCurT, and here, the band is DCur (1, 5). The secret data is embedded in the host images to make it secure, and then, the extraction of the embedding process takes place inversely. For experimentation analysis, 20 digital images are considered, and different attacks are applied in the proposed watermarking model. Thus, the watermarked image looks lossless compared with the host image. Moreover, recent literary works and domain transform are also utilized for strength investigation of the proposed watermarking model.

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Perceptual watermarking for Discrete Shearlet transform

Cited by 6 publications

References 14 publications

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory

Texture Directionality‐Based Digital Watermarking in Nonsubsample Shearlet Domain

Application of discrete transforms with selective coefficients for blind image watermarking

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