An efficient image steganography approach based on <scp>QR</scp> factorization and singular value decomposition in non‐subsampled contourlet transform domain

Yasmeen, Fauzia; Uddin, Mohammad Shorif

doi:10.1002/spy2.229

Cited by 3 publications

(1 citation statement)

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“…Due to the lack of information from the original sample in the virtual samples generated by the QR reconstruction method [33,34] , this article adopts the QR reconstruction weighted fusion method. It can maintain the basic properties of the matrix unchanged and the necessary information is not missing, while increasing the number of samples while ensuring the quality of virtual samples.…”

Section: Qr Decompositionmentioning

confidence: 99%

A new surface roughness measurement method based on QR-SVM

Yu,

Li,

Sheng

et al. 2023

Preprint

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This study proposes a method for detecting surface roughness in machining, which solves the problem of low detection accuracy caused by a small sample size based on machine vision detection. The fusion of QR and Support Vector Machine (SVM) methods is used to detect surface roughness. Firstly, a contact roughness detector is used to measure the surface roughness value, and a CCD is used to obtain the processed surface image to obtain the sample. Secondly, the QR decomposition method is improved to generate virtual samples and expand the sample size. Extract the texture feature values of the image using the gray level co-occurrence matrix, and establish the correlation between roughness and texture features. Finally, support vector machines are used to classify the surface roughness of mechanical machining. The experimental results show that the accuracy of the surface roughness detection method based on machine vision has increased from 80.6–96.5%, proving the feasibility of this method and providing a theoretical basis for on-site detection of small sample surface roughness. This method has certain engineering application potential.

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Section: Qr Decompositionmentioning

confidence: 99%

A new surface roughness measurement method based on QR-SVM

Yu,

Li,

Sheng

et al. 2023

Preprint

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Parallel BFS through pennant data structure with reducer hyper‐object based data hiding for 3D mesh images

Bandyopadhyay,

Mukherjee,

Mukhopadhyay

et al. 2024

Security and Privacy

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Data hiding refers to the technique employed to conceal sensitive data within various forms of multimedia, such as video, audio, text, 2D images, 3D images, and more, while also being able to successfully extract said information from the multimedia file. Although numerous data hiding techniques exist for 2D images, research on data hiding in 3D images is still in its early stages. Current 3D image steganography methods suffer from limitations in terms of embedding capacity and time complexity. To overcome these difficulties, we propose a novel 3D image steganographic technique using Parallel Breadth First Search (PBFS) with hyper‐object. Our approach utilizes the PBFS searching strategy, along with layer synchronization, to embed private information within the vertices of the 3D mesh images. To maximize the effectiveness of the process and minimize time, cost, complexity, and execution, we involved the data structure “bag” to parallelize the BFS. The implementation of bags is based on the pennant data structure. This methodology obtained an impressive Embedding Capacity (EC) of 9.00 bits per vertex (bpv) while maintaining superior visual quality and time complexity. Consequently, the proposed methodology holds great potential for widespread utilization across numerous sectors, including private and government organizations such as intelligence agencies, intellectual property rights management, cloud data security, defense, covert communication, medical imagery, and more.

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