A reliable and secure image watermarking algorithm using homomorphic transform in DWT domain

Khare, Priyank; Srivastava, Vinay Kumar

doi:10.1007/s11045-020-00732-1

Cited by 31 publications

(5 citation statements)

References 46 publications

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“…However, multiplicative noise has a multiplicative relationship with image signals, and the noise part changes with them. Multiplicative noise in images should be converted into additive noise by logarithmic transformation, 10 and the correlation between noise and signals should be removed. Then, the transformed image is denoised, and the final denoised image is obtained by combining the inverse exponential transformation.…”

Section: Homomorphism Transformationmentioning

confidence: 99%

Improved sparse representation algorithm for optical coherence tomography images

Miao

Zhou

Wang³

et al. 2022

Int J Imaging Syst Tech

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An improved sparse representation algorithm is proposed for reducing speckle noise in optical coherence tomography (OCT) images. To reduce the dictionary training time, the orthogonal matching pursuit algorithm (OMP) is replaced by a piecewise orthogonal matching pursuit algorithm (ST-OMP), and a threshold standard is set to train multiple atoms simultaneously to improve the tracking efficiency. Normal, diabetic macular edema, and fundus neovascularization membrane retina OCT images were tested using the proposed algorithm and other denoising algorithms. The results show that the proposed algorithm exhibits an excellent denoising ability. The details and edge information in the OCT images were well preserved by the proposed algorithm. Meanwhile, the image processing time was reduced by half with the proposed algorithm compared with the traditional K-SVD algorithm.

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Section: Homomorphism Transformationmentioning

confidence: 99%

Improved sparse representation algorithm for optical coherence tomography images

Miao

Zhou

Wang³

et al. 2022

Int J Imaging Syst Tech

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“…Previous studies [13,15,18] have selected LH, HL, and HH subbands for embedding watermarks because the LL subband contains significant information about the image. However, we choose the LL subband as an input of our decoder D and get the extract message with M ′ = D(W φ ).…”

Section: Embedding and Extracting Watermark On Dwtmentioning

confidence: 99%

An Analysis of Historical Neo-institutionalism on Changes of University Funding Policy

Jang¹

2018

Korea Univ Inst Educ Res

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The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains, but protecting their copyrights has not yet been researched in depth. Recently, NeRF watermarking has been considered one of the pivotal solutions for safely deploying NeRFbased 3D representations. However, existing methods are designed to apply only to implicit or explicit NeRF representations. In this work, we introduce an innovative watermarking method that can be employed in both representations of NeRF. This is achieved by fine-tuning NeRF to embed binary messages in the rendering process. In detail, we propose utilizing the discrete wavelet transform in the NeRF space for watermarking. Furthermore, we adopt a deferred back-propagation technique and introduce a combination with the patch-wise loss to improve rendering quality and bit accuracy with minimum trade-offs. We evaluate our method in three different aspects: capacity, invisibility, and robustness of the embedded watermarks in the 2D-rendered images. Our method achieves state-of-the-art performance with faster training speed over the compared state-of-the-art methods. Project page: https://kuailab.github.io/cvpr2024waterf/

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“…Wavelet transform has been widely used in watermarking algorithm because of its large number of branches. The watermarking algorithm based on discrete wavelet transform (DWT) and integer wavelet transform (IWT) has good robustness to most attacks [3][4][5][6], but poor robustness to geometric attacks [7][8][9]. Li et al [10] proposed a watermarking algorithm based on the combination of redistributed discrete wavelet transform (RI-DWT) and singular value decomposition, which effectively improved the defects of poor robustness of DWT and IWT against geometric attacks.…”

Section: Introductionmentioning

confidence: 99%

Blind watermarking algorithm based on redistributed invariant integer wavelet transform and BP network

Deng

2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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In this paper, a blind watermarking algorithm based on redistributed invariant integer wavelet transform (RI-IWT) and BP network is proposed. The host image is processed by RI-DWT and QR decomposition, and the watermark is embedded in the low frequency and high frequency sub-band of the host image, so as to increase the embedding capacity. Moreover, we realize the blind extraction of watermark through BP network. In the extraction stage, the host image information and part of the watermark information are no longer required, thus improving the security of the algorithm. Finally, we simulate the algorithm and compare it with the relative algorithms. Simulation results show that the proposed algorithm has high invisibility and good robustness against JPEG attacks, rotation attacks, noise attacks.

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A reliable and secure image watermarking algorithm using homomorphic transform in DWT domain

Cited by 31 publications

References 46 publications

Improved sparse representation algorithm for optical coherence tomography images

Improved sparse representation algorithm for optical coherence tomography images

An Analysis of Historical Neo-institutionalism on Changes of University Funding Policy

Blind watermarking algorithm based on redistributed invariant integer wavelet transform and BP network

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