Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
This addresses the need for ethical safeguards against data pollution in medical imaging, but it is incremental as it builds on existing watermarking methods.
The study tackled the problem of identifying AI-generated medical images by incorporating invisible watermarks and evaluated their impact on downstream classification tasks, finding that watermarks reduced classification accuracy by 5% while maintaining detectability.
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.