A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

Harvey, Bruce A.; Imran, Muhammad

doi:10.13164/re.2017.0823

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Hasan Demirel et al performed SVD decomposition on the low-frequency subbands of remote sensing satellites obtained by DWT to further enhance the illumination and prevent edge degradation [24] . Muhammad IMRAN et al used singular value decomposition technology in the extraction of watermarks, which can enhance the robustness of the algorithm [25] .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Yan

Yuan

et al. 2023

Preprint

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Aiming at the problems of blurring and distortion of traditional patterns, an enhancement method to improve resolution and contrast is proposed.First, the discrete wavelet transform, stationary wavelet transform, and interpolation algorithm are used to obtain high-resolution images of traditional patterns. Then, the improved singular value matrix coefficients and the reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. The experimental results show that the evaluation indexes such as mean square error, peak signal-to-noise ratio, and structural similarity corresponding to this method are improved compared with other comparison methods. It can effectively improve the quality of traditional patterns and is of great significance to the research on the restoration and protection of traditional patterns.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Yan

Yuan

et al. 2023

Preprint

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“…The technique ensures the confidentiality of the data and shows stronger robustness against different attacks compared with Refs. 77–82. However, a transparent investigation of security and cost is required.…”

Section: State-of-the-art Watermarking In Deep-learning Environmentsmentioning

confidence: 99%

Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information-especially in the form of images-from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.

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“…When any image whose inserted watermark is different from the restored watermark, can obtain the correct forged restored watermark, an FPP occurs. Moreover, the issue of FPP can be resolved using the principal component (PC) [17], [32], [50], [51], [53]. Hence, the high robustness, greater embedding strength, and resistance to a false-positive problem (FPP) with good perceptual quality of a watermarking scheme remain a major issue among researchers.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, researchers are also proposing watermark schemes within the frequency domain. The frequency-domain based algorithms has different applications, such as watermarking algorithms developed using frequency-domain operations like discrete wavelet transform (DWT) [1], [5], [13], [17], [24], [48], [50]. The frequency-domain of the cover data is originally broken down into different frequencies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Optimized Semi-Blind Scheme for Color Image Watermarking

2020

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Image watermarking is a robust solution for solving key issues like copyright protection and proof of ownership of digital data. Existing schemes of image watermarking mostly used grayscale or binary images as embedded watermarks, while only a few watermarking schemes are developed for color images. In this paper, we propose a novel robust semi-blind image watermarking scheme based on finite ridgelet transform (FRT), discrete wavelet transform (DWT), singular value decomposition (SVD), particle swarm optimization (PSO), and Arnold transform to protect copyright and verify the authenticity of color images. Firstly, the color image is converted from RGB to YCbCr color space, and the luminance component (Y) is taken into account to insert the watermark data. In this study, the principal component (PC) of the watermark image is directly inserted into the corresponding singular value of the cover image by the scaling factor to avoid the false positive problem (FPP). To further improve security, Arnold transform is applied to process the Y channel of the watermark image before inserting it in the cover image. Besides, PSO optimizes the embedding factor matrices. The qualitative evaluation factors like peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to assess the visual quality, while normalized correlation coefficient (NCC) is used to assess the resemblance between the watermarked and the restored watermarked images. The performance of the proposed scheme is evaluated using geometric, non-geometric, and combinational attacks, and its comparison is performed with different image watermarking schemes to prove its robustness.

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A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

Abstract: Abstract.A

Cited by 8 publications

References 18 publications

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Comprehensive review of watermarking techniques in deep-learning environments

A Novel Optimized Semi-Blind Scheme for Color Image Watermarking

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