Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2348907
Enhancing medical image security through machine learning and dual watermarking-based technique
Kumari Suniti Singh
; Department of Electronics Engineering, KNIT, Sultanpur, India
*
Harsh Vikram Singh
; Department of Electronics Engineering, KNIT, Sultanpur, India
* Dopisni autor.
Sažetak
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.
Ključne riječi
CT-scan images; dual watermarking; RDWT; HD; RSVD; image security; chaotic encryption; PSO optimization; DNN based denoising
Hrčak ID:
326435
URI
Datum izdavanja:
1.10.2024.
Posjeta: 0 *