# Adoption of Watermarking for Generative AI Systems in Practice and Implications under the new EU AI Act

arXiv: https://arxiv.org/abs/2503.18156 · HTML: https://mlsanity.com/paper/2503.18156/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 9 of 9)

- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (2023)
- [Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain](https://mlsanity.com/paper/2309.08133/) (2023)
- [A Representative Study on Human Detection of Artificially Generated Media Across Countries](https://mlsanity.com/paper/2312.05976/) (2023)
- [Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How](https://mlsanity.com/paper/2403.06823/) (2024)
- [What constitutes a Deep Fake? The blurry line between legitimate processing and manipulation under the EU AI Act](https://mlsanity.com/paper/2412.09961/) (2024)

## Built on by (most cited first, 7 of 7)

- [AI-Generated Video Detection via Perceptual Straightening](https://mlsanity.com/paper/2507.00583/) (2025)
- [Human-AI Governance (HAIG): A Trust-Utility Approach](https://mlsanity.com/paper/2505.01651/) (2025)
- [ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport](https://mlsanity.com/paper/2602.07235/) (2026)
- [Authenticated Contradictions from Desynchronized Provenance and Watermarking](https://mlsanity.com/paper/2603.02378/) (2026)
- [HeavyWater and SimplexWater: Distortion-Free LLM Watermarks for Low-Entropy Next-Token Predictions](https://mlsanity.com/paper/2506.06409/) (2025)
- [Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification](https://mlsanity.com/paper/2603.00177/) (2026)
- [Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents](https://mlsanity.com/paper/2605.08964/) (2026)

## Abstract (arXiv)

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.