评估不可见水印在AI生成医学图像中的有效性
Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
AI总结：
针对AI生成医学图像的识别难题，本研究提出为合成医学图像嵌入不可见水印并评估其对下游分类任务的效用，为提升合成医学图像可检测性、强化伦理及防范数据污染提供讨论基础。
摘要：
AI生成的医学图像因有望解决现实世界中的数据稀缺难题而日益普及。然而，如何准确识别这些合成图像仍是一个值得关注的问题，尤其是当它们与真实图像高度相似时。为应对这一挑战，DALLE、Imagen等图像生成器已集成数字水印，以辅助判别合成图像的真实性。这些水印嵌入图像像素中，人眼不可见但仍可被检测到。不过，目前尚缺乏针对此类不可见水印对合成医学图像效用潜在影响的全面研究。本研究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场景下的有效性。我们的目标是推动相关讨论，探讨此类水印在提升合成医学图像可检测性、强化伦理标准、防范数据污染及潜在欺诈方面的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
