Adoption of Watermarking Measures for AI-Generated Content and Implications under the EU AI Act

• URL: http://arxiv.org/abs/2503.18156v2
• Date: Tue, 03 Jun 2025 16:23:43 GMT
• Title: Adoption of Watermarking Measures for AI-Generated Content and Implications under the EU AI Act
• Authors: Bram Rijsbosch, Gijs van Dijck, Konrad Kollnig,
• Abstract summary: This paper provides an empirical analysis of 50 widely used AI systems for image generation, embedded into a legal analysis of the AI Act.<n>We find that only a minority of AI image generators currently implement adequate watermarking and deep fake labelling.
• Score: 4.2125200966193885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, particularly with the emergence of "deep fakes" that impersonate real individuals. 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 current status of the implementation of such measures remains largely unexamined. Moreover, the practical implications of the AI Act's watermarking and labelling requirements have not previously been studied. The present paper therefore both provides an empirical analysis of 50 widely used AI systems for image generation, embedded into a legal analysis of the AI Act. In our legal analysis, we identify four categories of generative AI image deployment scenarios relevant under the AI Act and outline how the legal obligations 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 (8%) 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 easy detection of watermarks in images.

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