# GenAI Confessions: Black-box Membership Inference for Generative Image Models

arXiv: https://arxiv.org/abs/2501.06399 · HTML: https://mlsanity.com/paper/2501.06399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 20 of 20)

- [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)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://mlsanity.com/paper/2305.18462/) (2023)
- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [Reconstruction and Membership Inference Attacks against Generative Models](https://mlsanity.com/paper/1906.03006/) (2019)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [Membership Inference Attacks Against Text-to-image Generation Models](https://mlsanity.com/paper/2210.00968/) (2022)
- [Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy](https://mlsanity.com/paper/2405.14800/) (2024)
- [Video Diffusion Models: A Survey](https://mlsanity.com/paper/2405.03150/) (2024)
- [Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models](https://mlsanity.com/paper/2310.19410/) (2023)
- [CDI: Copyrighted Data Identification in Diffusion Models](https://mlsanity.com/paper/2411.12858/) (2024)
- [Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2407.13252/) (2024)
- [Generative AI in Vision: A Survey on Models, Metrics and Applications](https://mlsanity.com/paper/2402.16369/) (2024)

## Built on by (most cited first, 2 of 2)

- [Auditing Training Data in Generative Music Models via Black-Box Membership Inference](https://mlsanity.com/paper/2605.29202/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

From a simple text prompt, generative-AI image models can create stunningly realistic and creative images bounded, it seems, by only our imagination. These models have achieved this remarkable feat thanks, in part, to the ingestion of billions of images collected from nearly every corner of the internet. Many creators have understandably expressed concern over how their intellectual property has been ingested without their permission or a mechanism to opt out of training. As a result, questions of fair use and copyright infringement have quickly emerged. We describe a method that allows us to determine if a model was trained on a specific image or set of images. This method is computationally efficient and assumes no explicit knowledge of the model architecture or weights (so-called black-box membership inference). We anticipate that this method will be crucial for auditing existing models and, looking ahead, ensuring the fairer development and deployment of generative AI models.