Robust transparent image watermarking with Shearlet transform and bidiagonal singular value decomposition

Mardanpour, Malihe; Chahooki, Mohammad Ali Zare

doi:10.1016/j.aeue.2016.03.004

Cited by 34 publications

(27 citation statements)

References 11 publications

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“…In one group secret data is embedded using an interpolation error and in another group secret data is embedded using histogram shifting in order to increase the embedding capacity. Using shearlet transform a robust watermarking technique is proposed by Mardanpour and Chachooki [11]. First, the image is divided into a number of subbands using shearlet transform.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

Shanmugapriya¹,

Santhi²,

Mohan³

2018

IJET

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In telemedicine, medical data are shared across the world among different specialists for various purposes through an unsecured medium. So there is a need to protect the medical data during transmission. With the help of image watermarking techniques, medical images are protected along with the electronic patient information (EPI). This paper proposes a medical image watermarking, by applying wavelet transform, using an interpolation technique. EPI data is embedded within the transformed medical image to generate a watermarked image. At the extraction side, EPI data are extracted and medical image is reconstructed without any loss. The performance of the proposed method is analyzed using a peak signal to noise ratio (PSNR), mean absolute error (MAE) and structural similarity index (SSIM). The experimental result shows that the proposed method gives better results.

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Section: Introductionmentioning

confidence: 99%

Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

Shanmugapriya¹,

Santhi²,

Mohan³

2018

IJET

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“…A robust watermarking technique prevents a watermark attack against geometric distortions, ensures the synchronisation of the watermark before and after embedding, and ensures watermark resilience to common image processing attacks as well as desynchronisation attacks (Huo-Chong et al 2011; Mardanpour and Chahooki 2016). Robust watermarking ensures self-synchronising schemes, which will certainly permit the recovery of the watermark after geometrical attacks (Li et al 2006).…”

Section: Introductionmentioning

confidence: 99%

An intermediate significant bit (ISB) watermarking technique using neural networks

2016

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Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

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“…In this section, the proposed algorithm is compared with methods based on the discrete wavelet transform domain (DWT-SVD) [20], nonsubsample contourlet transform domain (NSCT-SVD) [12], and shearlet transform domain (ST-SVD) [17]. Tables 10 and 11 and Figures 12 and 13 show a comparison of robustness for certain attacks on the four methods.…”

Section: Comparative Analysismentioning

confidence: 99%

“…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. Based on previous studies [17], a watermarking algorithm was proposed using the shearlet transform and bidiagonal singular value to achieve balance between robustness and imperceptibility.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Robust transparent image watermarking with Shearlet transform and bidiagonal singular value decomposition

Cited by 34 publications

References 11 publications

Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

An intermediate significant bit (ISB) watermarking technique using neural networks

Texture Directionality-Based Digital Watermarking in Nonsubsample Shearlet Domain

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