  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.
