# Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images

arXiv: https://arxiv.org/abs/2402.03473 · HTML: https://mlsanity.com/paper/2402.03473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Will Large-scale Generative Models Corrupt Future Datasets?](https://mlsanity.com/paper/2211.08095/) (2022)
- [You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images](https://mlsanity.com/paper/2305.18337/) (2023)

## Built on by (most cited first, 2 of 2)

- [When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting](https://mlsanity.com/paper/2507.03646/) (2025)
- [Robustness of Watermarking on Text-to-Image Diffusion Models](https://mlsanity.com/paper/2408.02035/) (2024)

## Abstract (arXiv)

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.