研究欧盟新规下生成式AI图像水印的落地现状与法律影响
Adoption of Watermarking for Generative AI Systems in Practice and Implications under the new EU AI Act
- 分类分析四类AI生成场景的法律义务
- 仅38%图像生成器实现有效水印，18%标注深度伪造
- 公开检测工具并提出改进方案，助力合规
近年来，AI生成图像质量已高到普通人难以辨别真伪。这一趋势结合网络上AI内容的快速传播，带来了诸多社会风险。水印技术通过在图像中嵌入信息以标识其为AI生成，成为应对该风险的核心手段。事实上，水印和AI标签措施已逐渐成为多国法律要求，包括2024年欧盟《人工智能法案》。尽管生成式图像系统广泛使用，其实际实施情况与法律影响仍缺乏深入研究。本文从法律与实证两方面展开分析：法律层面，识别出四类生成式AI部署场景并厘清对应义务；实证层面发现，仅有38%的图像生成器采用充分水印，仅18%执行深度伪造标注。为此，本文提出多项改进路径，并公开发布用于检测图像水印的工具。
原文摘要 · Abstract (English)
AI-generated images have become so good in recent years that individuals often cannot distinguish them any more from "real" images. This development, combined with the rapid spread of AI-generated content online, creates a series of societal risks. Watermarking, a technique that involves embedding information within images and other content to indicate their AI-generated nature, has emerged as a primary mechanism to address the risks posed by AI-generated content. Indeed, watermarking and AI labelling measures are now becoming a legal requirement in many jurisdictions, including under the 2024 European Union AI Act. Despite the widespread use of AI image generation systems, the practical implications and the current status of implementation of these measures remain largely unexamined. The present paper therefore provides both an empirical and a legal analysis of these measures. In our legal analysis, we identify four categories of generative AI deployment scenarios and outline how the legal obligations could apply in each category. In our empirical analysis, we find that only a minority number of AI image generators currently implement adequate watermarking (38%) and deep fake labelling (18%) practices. In response, we suggest a range of avenues of how the implementation of these legally mandated techniques can be improved, and publicly share our tooling for the detection of watermarks in images.
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