|
--- |
|
license: cc-by-4.0 |
|
language: |
|
- en |
|
tags: |
|
- art |
|
- text-to-image |
|
- stable-diffusion-diffusers |
|
- unlearned-diffusion-model |
|
- safe-diffusion-model |
|
- unlearned-text-encoder |
|
- defensive-unlearning |
|
--- |
|
|
|
|
|
# Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models |
|
### [Project Website]() | [Arxiv Preprint](https://arxiv.org/abs/2405.15234) | [Fine-tuned Weights](https://drive.google.com/drive/folders/1Nf-EJ2W3CsZwpc5blZFi7tm7o1wEiTg4?usp=sharing) | [Demo]() <br> |
|
Our proposed robust unlearning framework, AdvUnlearn, enhances diffusion models' safety by robustly erasing unwanted concepts through adversarial training, achieving an optimal balance between concept erasure and image generation quality. |
|
|
|
|
|
|
|
<div align="center"> |
|
<img src="https://cdn-uploads.huggingface.co/production/uploads/63e44e62789dcaae43c865d9/vad9l9ME0KD0OJKYLcFun.png" /> |
|
</div> |
|
|
|
|
|
## Baselines |
|
|
|
| DM Unlearning Methods | Nudity | Van Gogh | Objects | |
|
|:-------|:----:|:-------:| :-------:| |
|
| [ESD](https://github.com/rohitgandikota/erasing) (Erased Stable Diffusion) | ✅ | ✅ | ✅ |
|
| [FMN](https://github.com/SHI-Labs/Forget-Me-Not) (Forget-Me-Not) | ✅ | ✅ | ✅ |
|
| [AC](https://github.com/nupurkmr9/concept-ablation) (Ablating Concepts) | ❌ | ✅ | ❌ |
|
| [UCE](https://github.com/rohitgandikota/unified-concept-editing) (Unified Concept Editing) | ✅ | ✅ | ❌ |
|
| [SalUn](https://github.com/OPTML-Group/Unlearn-Saliency) (Saliency Unlearning) | ✅ | ❌ | ✅ |
|
| [SH](https://github.com/JingWu321/Scissorhands_ex) (ScissorHands) | ✅ | ❌ | ✅ |
|
| [ED](https://github.com/JingWu321/EraseDiff) (EraseDiff) | ✅ | ❌ | ✅ |
|
| [SPM](https://github.com/Con6924/SPM) (concept-SemiPermeable Membrane) | ✅ | ✅ | ✅ |
|
| **AdvUnlearn (Ours)** | ✅ | ✅ | ✅ |
|
|
|
|
|
<br> |
|
|
|
|
|
## Cite Our Work |
|
The preprint can be cited as follows: |
|
``` |
|
@misc{zhang2024defensive, |
|
title={Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models}, |
|
author={Yimeng Zhang and Xin Chen and Jinghan Jia and Yihua Zhang and Chongyu Fan and Jiancheng Liu and Mingyi Hong and Ke Ding and Sijia Liu}, |
|
year={2024}, |
|
eprint={2405.15234}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
|
|
--- |
|
license: cc-by-4.0 |
|
--- |
