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Steganography for medical record image.

Chunjun Hua1, Yue Wu2, Yiqiao Shi1

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Summary

This study introduces a novel image steganography model for electronic health records (EHR) to protect sensitive medical images. The model enhances security against cropping and transmission distortions, ensuring data privacy.

Keywords:
Digital image watermarkingElectronic health records systemImage steganographyMedical record image

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Area of Science:

  • Computer Science
  • Information Security
  • Medical Informatics

Background:

  • Electronic Health Record (EHR) systems contain sensitive medical images requiring robust data protection.
  • Existing image steganography methods may not adequately address the unique vulnerabilities of medical document images, such as cropping attacks.

Purpose of the Study:

  • To develop an advanced image steganography model tailored for medical record images within EHR systems.
  • To enhance the security and privacy of medical image data against common image manipulation and transmission errors.

Main Methods:

  • A novel steganographic model was developed based on the StegaStamp framework.
  • Text region segmentation and watermark region localization were implemented to mitigate cropping attacks.
  • A distortion network was incorporated to ensure robustness against communication-induced distortions.
  • The Fast Image Steganography Method (FISM) was integrated into the loss function to reduce visual artifacts.

Main Results:

  • The developed model demonstrated effective protection of medical record images against cropping attacks.
  • The distortion network proved successful in maintaining steganographic integrity during data transmission.
  • Integration of FISM significantly reduced ripple texture, improving steganographic image quality.
  • Experimental results confirmed high decoding accuracy and superior image quality for the proposed method.

Conclusions:

  • The proposed image steganography model offers a secure and robust solution for protecting medical record images in EHR systems.
  • The combination of text region segmentation, a distortion network, and FISM loss function effectively addresses the challenges of securing medical image data.
  • This approach enhances both the privacy and usability of medical images within digital health records.