# Steal My Artworks for Fine-tuning? A Watermarking Framework for Detecting Art Theft Mimicry in Text-to-Image Models

arXiv: https://arxiv.org/abs/2311.13619 · HTML: https://mlsanity.com/paper/2311.13619/

## Key facts

- Cited by 8 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 17 of its 27 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## 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, 17 of 17)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion](https://mlsanity.com/paper/2111.12417/) (2021)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Controllable Text-to-Image Generation](https://mlsanity.com/paper/1909.07083/) (2019)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Generating Images from Captions with Attention](https://mlsanity.com/paper/1511.02793/) (2015)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)

## Built on by (most cited first, 8 of 8)

- [Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models](https://mlsanity.com/paper/2410.10437/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13061/) (2025)
- [Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?](https://mlsanity.com/paper/2503.09122/) (2025)
- [Training Data Attribution: Was Your Model Secretly Trained On Data Created By Mine?](https://mlsanity.com/paper/2409.15781/) (2024)
- [Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes](https://mlsanity.com/paper/2602.22197/) (2026)
- [Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage](https://mlsanity.com/paper/2411.15367/) (2024)
- [UniMark: Unified Adaptive Multi-bit Watermarking for Autoregressive Image Generators](https://mlsanity.com/paper/2604.11843/) (2026)

## Abstract (arXiv)

The advancement in text-to-image models has led to astonishing artistic performances. However, several studios and websites illegally fine-tune these models using artists' artworks to mimic their styles for profit, which violates the copyrights of artists and diminishes their motivation to produce original works. Currently, there is a notable lack of research focusing on this issue. In this paper, we propose a novel watermarking framework that detects mimicry in text-to-image models through fine-tuning. This framework embeds subtle watermarks into digital artworks to protect their copyrights while still preserving the artist's visual expression. If someone takes watermarked artworks as training data to mimic an artist's style, these watermarks can serve as detectable indicators. By analyzing the distribution of these watermarks in a series of generated images, acts of fine-tuning mimicry using stolen victim data will be exposed. In various fine-tune scenarios and against watermark attack methods, our research confirms that analyzing the distribution of watermarks in artificially generated images reliably detects unauthorized mimicry.