Enhancing medical image security through machine learning and dual watermarking-based technique

As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their se...

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Bibliographic Details
Published in:Automatika 2024-10, Vol.65 (4), p.1579-1592
Main Authors: Singh, Kumari Suniti, Singh, Harsh Vikram
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Channel noise
chaotic encryption
CT-scan images
Digital imaging
DNN based denoising
dual watermarking
Encryption
Health care
Image enhancement
image security
Information management
Literature reviews
Machine learning
Medical imaging
Optimization
Particle swarm optimization
PSO optimization
RDWT
RSVD
Security
Singular value decomposition
Watermarking
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Summary:As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their security. That's where image security techniques come in. In this paper we have developed a new approach to secure e-patient records like DICOM images. By combining the redundant discrete wavelets transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD). We developed a robust and dual watermarking scheme. This scheme uses multiple watermarks, including Electronic Patient Record (EPR) as text and images, to ensure high-level authentication. In order to attain a balance between imperceptibility and robustness, a PSO-based optimization of scale factor is employed and Turbo code is utilized to encode the EPR and minimize channel noise. Additionally, the marked image undergoes encryption using a 3D chaotic-based encryption technique, and the extracted watermark is denoised through a deep neural network. The result shows that the proposed scheme is both secure and reliable. With this dual watermarking scheme, we have made great strides in securing e-patient records.
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2024.2348907