Several companies--such as Google, Microsoft, and OpenAI--have deployed
techniques to watermark AI-generated content to enable proactive detection.
However, existing literature mainly focuses on user-agnostic detection.
Attribution aims to further trace back the user of a generative-AI service who
generated a given content detected as AI-generated. Despite its growing
importance, attribution is largely unexplored. In this work, we aim to bridge
this gap by providing the first systematic study on watermark-based, user-aware
detection and attribution of AI-generated content. Specifically, we
theoretically study the detection and attribution performance via rigorous
probabilistic analysis. Moreover, we develop an efficient algorithm to select
watermarks for the users to enhance attribution performance. Both our
theoretical and empirical results show that watermark-based detection and
attribution inherit the accuracy and (non-)robustness properties of the
watermarking method.

通过严格的概率分析，我们第一次对基于水印的面向用户感知的 AI 生成内容的检测和归属性能进行了系统研究，并开发了有效的算法来选择用户的水印以提高归属性能。我们的理论和实证结果表明，基于水印的检测和归属性能具有水印方法的准确性和 (非) 稳健性的特性。