Securing Medical Data by Combining Encryption and Robust Blind Medical Image Watermarking Based on Zaslavsky Chaotic Map and DCT Coefficients

Balaska, Nawel; Belmeguenaï, Aïssa; Goutas, Ahcène; Ahmida, Zahir; Boumerdassi, Selma

doi:10.1007/s42979-021-01012-w

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…These insignificant areas are subsequently divided into blocks with dimensions of 4 4, denoted as ( ). The transformation coefficients of ( ) are then computed using the two-dimensional discrete cosine transform function (2D-DCT) 24 , resulting in the generation of ( ) blocks. The 2D-DCT for a matrix I (with dimensions M N) is calculated using the formula specified in Eq.…”

Section: The Proposed Methodsmentioning

confidence: 99%

High-capacity data hiding for medical images based on the mask-RCNN model

Saidi,

Tibermacine,

Elhadad

2024

Sci Rep

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This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

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Section: The Proposed Methodsmentioning

confidence: 99%

High-capacity data hiding for medical images based on the mask-RCNN model

Saidi,

Tibermacine,

Elhadad

2024

Sci Rep

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A robust blockchain-based watermarking using edge detection and wavelet transform

Mannepalli,

Richhariya,

Gupta

et al. 2024

Multimed Tools Appl

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Enhancing security of medical images using code-based intermittent encryption and convolutional neural network

Aruchamy,

Sundarrajan,

Choudhry

et al. 2024

The Computer Journal

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There has recently been a rise in the demand for telemedicine systems that securely and effectively transmit medical pictures. Cognitive radio (CR) significantly uses the unutilized spectrum by using the notion of spectrum sensing. Like certain other patient records, medical imaging data has strict requirements for security and anonymity. This makes sending healthcare picture information via an exposed system difficult because of the problems identified and the risks associated with massive data spillage. This study suggests a reliable CR technology with an image encryption technique to transmit medical images securely. In the proposed approach, the convolutional neural network method has been employed for complaisant spectrum sensing, where the Fusion Center trains the network for classification tasks using historical sensing data. Due to the proper training, the system runs in a time-slotted fashion. The proposed method provides an actor-critic transfer learning technique for a secondary user to select its processing method to raise confidence level while observing energy constraints. Finally, the numerical simulation results are examined to assess the suggested approaches under various configurations related to peak signal-to-noise ratio and structural similarity index which provide 90% more efficiency than the traditional simulated techniques.

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Securing Medical Data by Combining Encryption and Robust Blind Medical Image Watermarking Based on Zaslavsky Chaotic Map and DCT Coefficients

Cited by 6 publications

References 46 publications

High-capacity data hiding for medical images based on the mask-RCNN model

High-capacity data hiding for medical images based on the mask-RCNN model

A robust blockchain-based watermarking using edge detection and wavelet transform

Enhancing security of medical images using code-based intermittent encryption and convolutional neural network

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