A Blind Image Watermarking Method for Personal Medical Data Security

Soualmi, Abdallah; Alti, Adel; Laouamer, Lamri

doi:10.1109/icnas.2019.8807442

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Methods that require the original image are classified into non-blind watermarking [12,14,18,20]. On the other hand, methods that do not require the original image are classified into blind watermarking [13,19,21].…”

Section: Types Of Watermarking Methods Conventionalmentioning

confidence: 99%

“…Finally, the method converts the image back to a spatial-domain image, which is the watermarked image. Typically, discrete cosine transform (DCT) is used for the domain conversion [1,[8][9][10][11][12][13][14], however discrete wavelet transform (DWT) is also used in some studies [15][16][17][18][19]. As the frequency-domain embedding method converts the domain of the image twice, its processing time is longer than that of spatial-domain embedding methods.…”

Section: Digital Image Watermarkingmentioning

confidence: 99%

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Re‐shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

Yakoh

2021

IEEJ Transactions Elec Engng

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Watermarking is a technology to embed digital data into a digital image. In particular, blind watermarking is expected to ensure the authenticity and/or accountability of digital images without compromising the visual impression of viewing. A re‐shooting resistant blind watermarking method is proposed in this study. This method uses the features of a given image to select regions, which are modified to embed digital data. As the proposed embedding process preserves these image features, no markers are required to extract the embedded data from the watermarked image. Furthermore, based on the estimation of the watermarked image region, the method crops the re‐shot image before the extraction. Therefore, the proposed method works, even if the re‐shot image includes the outer area surrounding the watermarked image region. Moreover, the proposed method compensates for the geometric transformation of the re‐shot image based on the definition of the coordinate system, which is based on the distribution of the image feature points. Therefore, the proposed method works when the re‐shot image is taken diagonally in front of the watermarked image. Experimental results validated that the proposed method can extract the embedded digital data from re‐shot images of a watermarked image under considerable conditions of re‐shooting. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Types Of Watermarking Methods Conventionalmentioning

confidence: 99%

Section: Digital Image Watermarkingmentioning

confidence: 99%

Re‐shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

Yakoh

2021

IEEJ Transactions Elec Engng

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SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

Tazi

Dykstra²,

Rajivan

et al. 2022

Lecture Notes in Computer Science

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Sensitive information is intrinsically tied to interactions in healthcare, and its protection is of paramount importance for achieving high-quality patient outcomes. Research in healthcare privacy and security is predominantly focused on understanding the factors that increase the susceptibility of users to privacy and security breaches. To understand further, we systematically review 26 research papers in this domain to explore the existing user studies in healthcare privacy and security. Following the review, we conducted a card-sorting exercise, allowing us to identify 12 themes integral to this subject such as "Data Sharing," "Risk Awareness," and "Privacy." Further to the identification of these themes, we performed an in-depth analysis of the 26 research papers report on the insights into the discourse within the research community about healthcare privacy and security, particularly from the user perspective. MotivationSecurity and privacy integration in the healthcare domain is essential to protect patients' data [12], considering medical records include sensitive health and personal information. The healthcare industry is often a prime target for cybercriminals considering that these data sets could contain a plethora of sensitive information such as social security numbers, birth dates, employment information, emergency contacts, and insurance and billing data; these data are also notoriously difficult to monitor or safeguard after a breach [23]. Furthermore, healthcare data are lucrative on the black market. Sahi et al. noted

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High Capacity and Reversible Steganographic Technique with Authentication Capability

Ansari,

Hurrah,

Khan

et al. 2024

Communications in Computer and Information Science

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A Blind Image Watermarking Method for Personal Medical Data Security

Cited by 8 publications

References 16 publications

Re‐shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

Re‐shooting Resistant Blind Watermarking Framework Based on Feature Separation With Gaussian Mixture Model

SOK: Evaluating Privacy and Security Vulnerabilities of Patients’ Data in Healthcare

High Capacity and Reversible Steganographic Technique with Authentication Capability

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