# Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective

arXiv: https://arxiv.org/abs/2404.19382 · HTML: https://mlsanity.com/paper/2404.19382/

## Key facts

- Cited by 13 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 22 of its 35 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)
- LLM safety and security / [Vision-language and text-to-image safety](https://mlsanity.com/safety/vision-language-and-text-to-image-safety/)

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [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)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://mlsanity.com/paper/2211.05105/) (2022)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Unified Concept Editing in Diffusion Models](https://mlsanity.com/paper/2308.14761/) (2023)
- [SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation](https://mlsanity.com/paper/2310.12508/) (2023)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.17591/) (2023)
- [Red-Teaming the Stable Diffusion Safety Filter](https://mlsanity.com/paper/2210.04610/) (2022)
- [Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?](https://mlsanity.com/paper/2310.10012/) (2023)
- [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023)
- [Circumventing Concept Erasure Methods For Text-to-Image Generative Models](https://mlsanity.com/paper/2308.01508/) (2023)
- [Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models](https://mlsanity.com/paper/2309.02218/) (2023)

## Built on by (most cited first, 13 of 13)

- [Direct Unlearning Optimization for Robust and Safe Text-to-Image Models](https://mlsanity.com/paper/2407.21035/) (2024)
- [Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts](https://mlsanity.com/paper/2410.12777/) (2024)
- [Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models](https://mlsanity.com/paper/2409.05668/) (2024)
- [Unveiling Concept Attribution in Diffusion Models](https://mlsanity.com/paper/2412.02542/) (2024)
- [Image Can Bring Your Memory Back: A Novel Multi-Modal Guided Attack against Image Generation Model Unlearning](https://mlsanity.com/paper/2507.07139/) (2025)
- [Rethinking Machine Unlearning in Image Generation Models](https://mlsanity.com/paper/2506.02761/) (2025)
- [Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://mlsanity.com/paper/2607.07907/) (2026)
- [AEGIS: Adversarial Target-Guided Retention-Data-Free Robust Concept Erasure from Diffusion Models](https://mlsanity.com/paper/2602.06771/) (2026)
- [SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation](https://mlsanity.com/paper/2606.01481/) (2026)
- [Rethinking the Vulnerability of Concept Erasure and a New Method](https://mlsanity.com/paper/2502.17537/) (2025)
- [RUB: Evaluating Residual Knowledge in Unlearned Models](https://mlsanity.com/paper/2504.14798/) (2025)
- [ReLAPSe: Reinforcement-Learning-trained Adversarial Prompt Search for Erased concepts in unlearned diffusion models](https://mlsanity.com/paper/2602.00350/) (2026)
- [Concept Score Relearning: A Unified Cross-Architecture Attack on Concept Erasure](https://mlsanity.com/paper/2609.33445/) (2026)

## Abstract (arXiv)

Advanced text-to-image diffusion models raise safety concerns regarding identity privacy violation, copyright infringement, and Not Safe For Work content generation. Towards this, unlearning methods have been developed to erase these involved concepts from diffusion models. However, these unlearning methods only shift the text-to-image mapping and preserve the visual content within the generative space of diffusion models, leaving a fatal flaw for restoring these erased concepts. This erasure trustworthiness problem needs probe, but previous methods are sub-optimal from two perspectives: (1) Lack of transferability: Some methods operate within a white-box setting, requiring access to the unlearned model. And the learned adversarial input often fails to transfer to other unlearned models for concept restoration; (2) Limited attack: The prompt-level methods struggle to restore narrow concepts from unlearned models, such as celebrity identity. Therefore, this paper aims to leverage the transferability of the adversarial attack to probe the unlearning robustness under a black-box setting. This challenging scenario assumes that the unlearning method is unknown and the unlearned model is inaccessible for optimization, requiring the attack to be capable of transferring across different unlearned models. Specifically, we employ an adversarial search strategy to search for the adversarial embedding which can transfer across different unlearned models. This strategy adopts the original Stable Diffusion model as a surrogate model to iteratively erase and search for embeddings, enabling it to find the embedding that can restore the target concept for different unlearning methods. Extensive experiments demonstrate the transferability of the searched adversarial embedding across several state-of-the-art unlearning methods and its effectiveness for different levels of concepts.