# Mist: Towards Improved Adversarial Examples for Diffusion Models

arXiv: https://arxiv.org/abs/2305.12683 · HTML: https://mlsanity.com/paper/2305.12683/

## Key facts

- Cited by 94 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 12 of its 13 references that are in the map.
- First posted on arXiv 22 May 2023.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 12 of 12)

- [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)
- [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)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples](https://mlsanity.com/paper/2302.04578/) (2023)
- [Raising the Cost of Malicious AI-Powered Image Editing](https://mlsanity.com/paper/2302.06588/) (2023)
- [Anti-DreamBooth: Protecting users from personalized text-to-image synthesis](https://mlsanity.com/paper/2303.15433/) (2023)

## Built on by (most cited first, 30 of 94)

- [Toward effective protection against diffusion based mimicry through score distillation](https://mlsanity.com/paper/2311.12832/) (2023)
- [MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning](https://mlsanity.com/paper/2311.13127/) (2023)
- [Copyright Protection in Generative AI: A Technical Perspective](https://mlsanity.com/paper/2402.02333/) (2024)
- [Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey](https://mlsanity.com/paper/2408.03400/) (2024)
- [Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models](https://mlsanity.com/paper/2404.09401/) (2024)
- [Targeted Attack Improves Protection against Unauthorized Diffusion Customization](https://mlsanity.com/paper/2310.04687/) (2023)
- [IMMA: Immunizing text-to-image Models against Malicious Adaptation](https://mlsanity.com/paper/2311.18815/) (2023)
- [Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies](https://mlsanity.com/paper/2405.19424/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [A Gray-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse](https://mlsanity.com/paper/2408.10901/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption](https://mlsanity.com/paper/2503.10081/) (2025)
- [Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think](https://mlsanity.com/paper/2404.13320/) (2024)
- [DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing](https://mlsanity.com/paper/2411.17957/) (2024)
- [AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing](https://mlsanity.com/paper/2512.07247/) (2025)
- [Imperceptible Protection against Style Imitation from Diffusion Models](https://mlsanity.com/paper/2403.19254/) (2024)
- [Text-to-Image Synthesis: A Decade Survey](https://mlsanity.com/paper/2411.16164/) (2024)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [FaceShield: Defending Facial Image against Deepfake Threats](https://mlsanity.com/paper/2412.09921/) (2024)
- [StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations](https://mlsanity.com/paper/2505.18766/) (2025)
- [Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation](https://mlsanity.com/paper/2506.01591/) (2025)
- [Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing](https://mlsanity.com/paper/2509.23279/) (2025)
- [U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI](https://mlsanity.com/paper/2406.15386/) (2024)
- [Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models](https://mlsanity.com/paper/2412.11423/) (2024)
- [TarPro: Targeted Protection against Malicious Image Editing](https://mlsanity.com/paper/2503.13994/) (2025)
- [DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing](https://mlsanity.com/paper/2504.17894/) (2025)
- [Anti-I2V: Safeguarding your photos from malicious image-to-video generation](https://mlsanity.com/paper/2603.24570/) (2026)
- [PID: Prompt-Independent Data Protection Against Latent Diffusion Models](https://mlsanity.com/paper/2406.15305/) (2024)
- [CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models](https://mlsanity.com/paper/2411.13144/) (2024)
- [Enhancing Variational Autoencoders with Smooth Robust Latent Encoding](https://mlsanity.com/paper/2504.17219/) (2025)

## Abstract (arXiv)

Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright. For example, infringers can make profits by imitating non-authorized human-created paintings with DMs. Recent researches suggest that various adversarial examples for diffusion models can be effective tools against these copyright infringements. However, current adversarial examples show weakness in transferability over different painting-imitating methods and robustness under straightforward adversarial defense, for example, noise purification. We surprisingly find that the transferability of adversarial examples can be significantly enhanced by exploiting a fused and modified adversarial loss term under consistent parameters. In this work, we comprehensively evaluate the cross-method transferability of adversarial examples. The experimental observation shows that our method generates more transferable adversarial examples with even stronger robustness against the simple adversarial defense.