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Machine learning based multipurpose medical image watermarking.

Rishi Sinhal1, Irshad Ahmad Ansari1

  • 1Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India.

Neural Computing & Applications
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a secure medical image watermarking method using deep neural networks. It ensures data integrity and allows for complete recovery of sensitive regions, enhancing medical data security.

Keywords:
Image authenticationMedical image watermarkingMultipurpose watermarkingOwnership verificationROI reversibility

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Area of Science:

  • Computer Science
  • Medical Imaging
  • Cryptography

Background:

  • Digital data security is critical, especially for sensitive medical images.
  • Unauthorized access and manipulation of medical data can lead to diagnostic errors and patient harm.
  • Existing security measures may lack robustness or reversibility for medical image applications.

Purpose of the Study:

  • To develop a blind and robust medical image watermarking framework using deep neural networks.
  • To ensure the integrity and confidentiality of medical images during sharing and storage.
  • To provide a reversible method for recovering the region of interest (ROI) data.

Main Methods:

  • A novel watermarking framework combining LZW compression, Integer Wavelet Transform (IWT), and SHA-256 hash keys.
  • Embedding a robust watermark in the original image and a fragile watermark (containing ROI recovery data and hash keys) in the region of non-interest (RONI).
  • Utilizing a deep neural network for efficient and robust watermark extraction and authentication.

Main Results:

  • The proposed scheme demonstrates significant imperceptibility and robust watermark extraction.
  • Achieved correct authentication and a completely reversible nature for ROI recovery.
  • Simulation results indicate superior performance compared to existing watermarking schemes.

Conclusions:

  • The developed deep neural network-based watermarking framework offers an effective security solution for medical images.
  • The method ensures data integrity, confidentiality, and complete reversibility, crucial for medical applications.
  • This approach significantly enhances the security and reliability of medical image data management.