# ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models arXiv: https://arxiv.org/abs/2504.13061 ยท HTML: https://mlsanity.com/paper/2504.13061/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. - Builds on 41 of its 75 references that are in the map. - First posted on arXiv 17 Apr 2025. ## Placed in - Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/) ## Builds on (most cited first, 30 of 41) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023) - [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022) - [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021) - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022) - [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022) - [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016) - [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022) - [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019) - [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021) - [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022) - [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017) - [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015) - [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022) - [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021) - [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023) - [A study of the effect of JPG compression on adversarial images](https://mlsanity.com/paper/1608.00853/) (2016) - [Are Diffusion Models Vulnerable to Membership Inference Attacks?](https://mlsanity.com/paper/2302.01316/) (2023) - [Anti-DreamBooth: Protecting users from personalized text-to-image synthesis](https://mlsanity.com/paper/2303.15433/) (2023) - [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022) - [Membership Inference Attacks against Diffusion Models](https://mlsanity.com/paper/2302.03262/) (2023) - [The ArtBench Dataset: Benchmarking Generative Models with Artworks](https://mlsanity.com/paper/2206.11404/) (2022) - [An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization](https://mlsanity.com/paper/2305.18355/) (2023) - [White-box Membership Inference Attacks against Diffusion Models](https://mlsanity.com/paper/2308.06405/) (2023) - [Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?](https://mlsanity.com/paper/2312.00084/) (2023) - [User Inference Attacks on Large Language Models](https://mlsanity.com/paper/2310.09266/) (2023) - [The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks](https://mlsanity.com/paper/2310.15469/) (2023) - [IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI](https://mlsanity.com/paper/2310.19248/) (2023) ## Built on by (most cited first, 5 of 5) - [StyleSentinel: Reliable Artistic Copyright Verification via Stylistic Fingerprints](https://mlsanity.com/paper/2508.01335/) (2025) - [VICTOR: Dataset Copyright Auditing in Video Recognition Systems](https://mlsanity.com/paper/2512.14439/) (2025) - [Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks](https://mlsanity.com/paper/2608.28671/) (2026) - [Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models](https://mlsanity.com/paper/2609.01723/) (2026) - [InceptionRAG: Stealthy Poisoning Attack Against Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.16818/) (2026) ## Abstract (arXiv) Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable. To this end, we propose a novel method for data-use auditing in the text-to-image generation model. The general idea of ArtistAuditor is to identify if a suspicious model has been finetuned using the artworks of specific artists by analyzing the features related to the style. Concretely, ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style. Then, ArtistAuditor queries a trained discriminator to gain the auditing decisions. The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values (> 0.937). By studying ArtistAuditor's transferability and core modules, we provide valuable insights into the practical implementation. Finally, we demonstrate the effectiveness of ArtistAuditor in real-world cases by an online platform Scenario. ArtistAuditor is open-sourced at https://github.com/Jozenn/ArtistAuditor.