# Medical Image Data Provenance for Medical Cyber-Physical System

arXiv: https://arxiv.org/abs/2403.15522 · HTML: https://mlsanity.com/paper/2403.15522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 59 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses](https://mlsanity.com/paper/2005.07359/) (2020)
- [Enhancement attacks in biomedical machine learning](https://mlsanity.com/paper/2301.01885/) (2023)

## Built on by (most cited first, 1 of 1)

- [X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging](https://mlsanity.com/paper/2602.09284/) (2026)

## Abstract (arXiv)

Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile applications. To mitigate these risks, this study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images, ensuring data provenance. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage, thus enabling verification of image authenticity and source. Moreover, a robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems. Through a case study on mobile fundus imaging, the effectiveness of the proposed framework is evaluated in terms of computational efficiency, image quality, security, and trustworthiness. This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.