Robust and Imperceptible Digital Image Watermarking Based on DWT-DCT-Schur

Sripradha, R.; Deepa, K.

doi:10.1007/978-981-16-0425-6_25

Cited by 2 publications

(4 citation statements)

References 9 publications

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“…Therefore, embedding the information to be hidden in the digital image of wavelet transform without destroying the original image is convenient. Therefore, the experiments in this study are performed using the low‐frequency subband of the more stable signal of the second‐level wavelet decomposition, which can greatly reduce the image data and facilitate feature point extraction 32 …”

Section: Methodsmentioning

confidence: 99%

“…The PSNR of the reconstructed image is used to measure the invisibility of the image (watermark carrier data) after embedding the watermark, 32 which is calculated as follows:

PSNR=10lo{g}_{10}\left(\frac{{({2}^{n}-1)}^{2}}{MSE}\right),

MSE=\frac{1}{H\unicode{x0200A}\ast \unicode{x0200A}W}\sum _{i=1}^{H}\sum _{j=1}^{W}((X(i,j)-Y(i,j))),

where

n

is the number of bits per sampled value,

MSE

is the mean square error between the original image and the processed image,

H

and

W

denote the numbers of image pixel size,

X(i,j)

is the pixel point in the i th row and j th column of the original image (watermark carrier data), and

Y(i,j)

is the pixel point in the i th row and j th column of the reconstructed image (reconstructed image). The higher the PSNR, the clearer the reconstructed image.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the experiments in this study are performed using the low-frequency subband of the more stable signal of the second-level wavelet decomposition, which can greatly reduce the image data and facilitate feature point extraction. 32 Little Boogie: The wavelet functions obtained from the wavelet analysis are diverse, and the performance of different effects of the images is influenced by the selection of different mother wavelets in the transform. In this experiment, Haar, dbN, symN, coifN, and dmeyer wavelets are used for experimental comparison and analysis.…”

Section: Overviewmentioning

confidence: 99%

“…The PSNR of the reconstructed image is used to measure the invisibility of the image (watermark carrier data) after embedding the watermark, 32 which is calculated as follows:…”

Section: Experiments 41 | Evaluation Metricsmentioning

confidence: 99%

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Digital watermarking method for image feature point extraction and analysis

Shan

Zhou

Zhang

et al. 2022

Int J of Intelligent Sys

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Multimedia information (e.g., photos) is subject to numerous attacks when extensively dispersed, excellent stealth and robustness play a critical role in information concealment. Next, to further improve the imperceptibility of the generated images, we introduce an information embedding based on feature extraction, since the feature points themselves are robust as strong corner points in the images. We will perform specific practical applications in information-hiding scenarios, such as ticket anticounterfeiting, copyright protection, tampering hints, and covert marking. As a result, while assuring the accuracy of hidden data, assault resistance must also be addressed. In this paper, a strategy for watermark embedding utilizing image feature points is proposed. In principle, the embedding and extraction of digital watermarks are achieved in the transform domain by discrete wavelet transform processing to make digital images more robust than before. The effectiveness of the algorithm is analyzed by experimental data, in which different wavelet bases are used. To check the robustness of the watermarking algorithm, various attacks are performed on watermarked images.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The PSNR of the reconstructed image is used to measure the invisibility of the image (watermark carrier data) after embedding the watermark, 32 which is calculated as follows:

PSNR=10lo{g}_{10}\left(\frac{{({2}^{n}-1)}^{2}}{MSE}\right),

MSE=\frac{1}{H\unicode{x0200A}\ast \unicode{x0200A}W}\sum _{i=1}^{H}\sum _{j=1}^{W}((X(i,j)-Y(i,j))),

where

n

is the number of bits per sampled value,

MSE

is the mean square error between the original image and the processed image,

H

and

W

denote the numbers of image pixel size,

X(i,j)

is the pixel point in the i th row and j th column of the original image (watermark carrier data), and

Y(i,j)

is the pixel point in the i th row and j th column of the reconstructed image (reconstructed image). The higher the PSNR, the clearer the reconstructed image.…”

Section: Methodsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

“…The PSNR of the reconstructed image is used to measure the invisibility of the image (watermark carrier data) after embedding the watermark, 32 which is calculated as follows:…”

Section: Experiments 41 | Evaluation Metricsmentioning

confidence: 99%

See 2 more Smart Citations