Security Attack on Secret Sharing Based Watermarking Using Fractional Fourier Transform and Singular Value Decomposition

Rosiyadi, Didi; Prasetyo, Heri; Horng, Shi–Jinn; Basuki, Akbari Indra

doi:10.1109/icramet51080.2020.9298671

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Intellectual Copyright Protection entails the creation of technologies that prohibit the pretreatment, alteration and dissemination of unique information without the owner’s authorization. To a specific level, acquiring and reproducing restricted technology helps the owner to conduct a detailed analysis on riskless and unlawful information alteration (Ernawan et al, 2020; Ramkumar and Manikandan, 2014; Rosiyadi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles

Nanammal

Jayagopalan²

2022

Concurrent Engineering

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Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the Support Vector Classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital; the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to processing these images and identifying the Pneumonia disease effectively as well as the proposed watermarking enabled security features provide a good impact in the medical field protection system. The resulting section provides the proper proof to the effectiveness of the proposed approach and its prediction efficiency.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles

Nanammal

Jayagopalan²

2022

Concurrent Engineering

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“…The proposed method is suitable to be adopted for bandwidthlimited image transmission as well as in the network requiring the privacy preservation [18], and packet filtering network [19]. The proposed method can also be extended into another applications such as in the image steganography [20], image watermarking [21], etc. Herein, the proposed method can be utilized to reconstruct or estimate the original image from the stego-image or watermarked image.…”

Section: Introductionmentioning

confidence: 99%

Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

Prasetyo¹,

Prasetyo²,

Prayuda³

et al. 2022

Journal of Internet Technology

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<p>The image companding is a simple image compression technique which is very easy to be implemented in the bandwidth-limited environment. This paper presents a simple way for improving the quality of decompressed image in the image companding task. The proposed method consists of two networks, namely Sub-band Network (SubNet) and Pixel Network (PixNet), for performing an image reconstruction. The SubNet module exploits the effectiveness of Stationary Wavelet Transform (SWT) and Convolutional Neural Network (CNN) in order to recover the lost information in the wavelet sub-bands basis. Whilst, the PixNet part applies CNN with identity mapping to improve the quality of initial reconstructed image obtained from the SubNet module. As reported in this paper, the proposed method outperforms the former existing schemes in the image companding task. It has also been proven that the proposed method is able to improve the quality of reconstructed image with some simple steps.</p> <p> </p>

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Hiding patient information in medical images : an encrypted dual image reversible and secure patient data hiding algorithm for E-healthcare

Bhardwaj

2021

Multimed Tools Appl

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Security Attack on Secret Sharing Based Watermarking Using Fractional Fourier Transform and Singular Value Decomposition

Cited by 6 publications

References 10 publications

RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles

RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles

Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

Hiding patient information in medical images : an encrypted dual image reversible and secure patient data hiding algorithm for E-healthcare

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