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.

翻译：人工智能生成的医学图像因有望解决现实世界数据稀缺问题而日益流行。然而，精确识别这些合成图像（尤其是它们与真实图像表现出惊人相似性时）的问题仍令人担忧。为缓解这一挑战，DALLE、Imagen等图像生成器已集成数字水印，旨在帮助辨别合成图像的真伪。这些水印嵌入图像像素中，人眼不可见，同时保持可检测性。然而，目前尚缺乏关于这些不可见水印对合成医学图像实用性的潜在影响的全面研究。在本研究中，我们提出将不可见水印嵌入合成医学图像，并评估其在下游分类任务中的有效性。我们的目标是为探讨此类水印在提升合成医学图像可检测性、加强伦理标准以及防范数据污染和潜在欺诈方面的可行性铺平道路。