# White-box Membership Inference Attacks against Diffusion Models

arXiv: https://arxiv.org/abs/2308.06405 · HTML: https://mlsanity.com/paper/2308.06405/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 49 of its 76 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)
- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 30 of 49)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [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)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)

## Built on by (most cited first, 30 of 42)

- [Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety](https://mlsanity.com/paper/2502.05206/) (2025)
- [Black-box Membership Inference Attacks against Fine-tuned Diffusion Models](https://mlsanity.com/paper/2312.08207/) (2023)
- [Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey](https://mlsanity.com/paper/2408.03400/) (2024)
- [Membership Inference Attacks on Diffusion Models via Quantile Regression](https://mlsanity.com/paper/2312.05140/) (2023)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models](https://mlsanity.com/paper/2402.11989/) (2024)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models](https://mlsanity.com/paper/2412.05538/) (2024)
- [Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss](https://mlsanity.com/paper/2402.05453/) (2024)
- [CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion](https://mlsanity.com/paper/2403.11162/) (2024)
- [DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation](https://mlsanity.com/paper/2503.22988/) (2025)
- [ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13061/) (2025)
- [Membership Inference Attacks Against Fine-tuned Diffusion Language Models](https://mlsanity.com/paper/2601.20125/) (2026)
- [CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion](https://mlsanity.com/paper/2409.07025/) (2024)
- [Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting](https://mlsanity.com/paper/2410.16657/) (2024)
- [Score-based Membership Inference on Diffusion Models](https://mlsanity.com/paper/2509.25003/) (2025)
- [Membership Inference Attacks on Tokenizers of Large Language Models](https://mlsanity.com/paper/2510.05699/) (2025)
- [Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models](https://mlsanity.com/paper/2502.02970/) (2025)
- [How does Bayesian Sampling help Membership Inference Attacks?](https://mlsanity.com/paper/2503.07482/) (2025)
- [Latent Diffusion Inversion Requires Understanding the Latent Space](https://mlsanity.com/paper/2511.20592/) (2025)
- [Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?](https://mlsanity.com/paper/2511.20710/) (2025)
- [Inference Attacks Against Graph Generative Diffusion Models](https://mlsanity.com/paper/2601.03701/) (2026)
- [MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data](https://mlsanity.com/paper/2603.19185/) (2026)
- [Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models](https://mlsanity.com/paper/2410.03039/) (2024)
- [Real-World Benchmarks Make Membership Inference Attacks Fail on Diffusion Models](https://mlsanity.com/paper/2410.03640/) (2024)
- [Identity-Focused Inference and Extraction Attacks on Diffusion Models](https://mlsanity.com/paper/2410.10177/) (2024)
- [Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective](https://mlsanity.com/paper/2505.20955/) (2025)
- [SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation](https://mlsanity.com/paper/2506.19360/) (2025)
- [Assessing the Effectiveness of Membership Inference on Generative Music](https://mlsanity.com/paper/2512.21762/) (2025)
- [Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbation](https://mlsanity.com/paper/2602.01645/) (2026)

## Abstract (arXiv)

Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance. The complex architecture of these models furnishes an extensive array of attack features. In light of this, we aim to design membership inference attacks (MIAs) catered to diffusion models. We first conduct an exhaustive analysis of existing MIAs on diffusion models, taking into account factors such as black-box/white-box models and the selection of attack features. We found that white-box attacks are highly applicable in real-world scenarios, and the most effective attacks presently are white-box. Departing from earlier research, which employs model loss as the attack feature for white-box MIAs, we employ model gradients in our attack, leveraging the fact that these gradients provide a more profound understanding of model responses to various samples. We subject these models to rigorous testing across a range of parameters, including training steps, sampling frequency, diffusion steps, and data variance. Across all experimental settings, our method consistently demonstrated near-flawless attack performance, with attack success rate approaching 100% and attack AUCROC near 1.0. We also evaluate our attack against common defense mechanisms, and observe our attacks continue to exhibit commendable performance.