Non-blind RGB watermarking approach using SVD in translation invariant wavelet space with enhanced Grey-wolf optimizer

Dappuri, Bhasker; Rao, M. Purnachandra; Sikha, Madhu Babu

doi:10.1007/s11042-020-09433-0

Cited by 18 publications

(3 citation statements)

References 20 publications

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“…The GWO is introduced different optimization problems in the image processing domain like joint denoising and unmixing of multispectral images [238], camera calibration method [239], multi-level image thresholding [212], multimodal image registration [279], classification of magnetic resonance brain images [280], image segmentation [191], non-blind RGB watermarking [281], sub-pixel displacement measurement [169], and key points selected to simplify the point cloud [282].…”

Section: Applications Of Grey Wolf Optimizermentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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Section: Applications Of Grey Wolf Optimizermentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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“…In Reference 31, authors proposed an optimized blind image watermarking using RDWT in PCA domain with improved gray‐wolf optimizer. Recently, few researchers implemented optimization‐based RDWT‐SVD approach for non‐blind color image watermarking applications 32,33 . Although, there are several blind and non‐blind image watermarking applications presented with the usage of RDWT in combination with other MR transforms, none of the literature supports the BVW applications.…”

Section: Introductionmentioning

confidence: 99%

A new video watermarking using redundant discrete wavelet in singular value decomposition domain with multi‐objective optimization

Kumar¹,

Srinivas²,

Prabhakar

2021

Concurrency and Computation

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Day by day, the transmission or sharing of digital information online is increasing due to the fact that Internet usage has become somewhat of an addiction for the majority of people. This results in larger copyright violation problems. Nowadays, most of the data or information is being transmitted in the form of digital images or videos, which are rather simple for the violators of copyright to forge or fake and then share for profit. Thus, digital watermarking came into existence as a possible solution to address these violations of copyright. This article proposes a new blind video watermarking (BVW) using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain, which utilizes the advantages of both RDWT and SVD to embed and extract the watermark information into the cover video without degrading the quality of the watermark. In addition, an efficient meta‐heuristic optimization with multi‐objective optimization (MOO) is employed to further optimize the proposed RDWT‐SVD approach. Further, various attacks were applied to the watermarked frame to disclose the robustness of proposed BVW using RDWT‐SVD with MOO approach. Extensive simulations on several test videos with comparison to the conventional BVW methodologies exhibit the transcendency of the proposed BVW using RDWT‐SVD with MOO approach in terms of watermarking quality evaluation metrics such as peak signal‐to‐noise ratio (PSNR), structural similarity index, normalized correlation, and even that of root mean square error as well.

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“…A nice review of the SVD-based image processing techniques is presented by Sadek (2012). Other important specific applications include image watermarking schemes (Dappuri, Rao and Sikha, 2020;Meenakshi, Swaraja and Kora, 2020;Thakkar and Srivastava, 2017), signal denoising and feature enhancements (Zhao and Jia, 2017), audio watermarking (Özer, Sankur and Memon, 2005;Rezaei and Khalili, 2019), sound source localization (Grondin and Glass, 2019a,b), sound recovery techniques (Zhang et al, 2016), etc. Moreover, SVD is recently being used extensively to solve different problems in bioinformatics, which include the analysis of protein functional associations (Franceschini et al, 2016), clustering for gene expression analysis (Horn and Axel, 2003;Liang, 2007;Bustamam, Formalidin and Siswantining, 2018), protein coding region prediction (Das, Das and Nanda, 2017), etc.…”

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confidence: 99%

A New Robust Scalable Singular Value Decomposition Algorithm for Video Surveillance Background Modelling

Roy¹,

Basu²,

Ghosh³

2021

Preprint

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A basic algorithmic task in automated video surveillance is to separate background and foreground objects. Camera tampering, noisy videos, low frame rate, etc., pose difficulties in solving the problem. A general approach which classifies the tampered frames, and performs subsequent analysis on the remaining frames after discarding the tampered ones, results in loss of information. We propose a robust singular value decomposition (SVD) approach based on the density power divergence to perform background separation robustly even in the presence of tampered frames. We also provide theoretical results and perform simulations to validate the superiority of the proposed method over the few existing robust SVD methods. Finally, we indicate several other use-cases of the proposed method to show its general applicability to a large range of problems.

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Non-blind RGB watermarking approach using SVD in translation invariant wavelet space with enhanced Grey-wolf optimizer

Cited by 18 publications

References 20 publications

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

A new video watermarking using redundant discrete wavelet in singular value decomposition domain with multi‐objective optimization

A New Robust Scalable Singular Value Decomposition Algorithm for Video Surveillance Background Modelling

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