Watermark-based Detection and Attribution of AI-Generated Image
Keywords: Image Watermark, AI-generated image, Watermark detection, Watermark attribution
TL;DR: The first systematic study on the theory, algorithm, and evaluation of watermark-based detection and attribution of AI-generated images
Abstract: Several companies--such as Google, Microsoft, and OpenAI--have deployed techniques to watermark AI-generated images to enable proactive detection. However, existing literature mainly focuses on user-agnostic detection. Attribution aims to further trace back the user who generated a detected AI-generated image. 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 images. 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.
Primary Area: generative models
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Submission Number: 7832
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