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Sponsored by
ZenMux
OPTML-Group / AdvUnlearn
Model's Last Updated: May 28 2024
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Introduction of AdvUnlearn
Model Details of AdvUnlearn
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Paper can be checked in
Arxiv Preprint
.
Code can be checked in
GitHub
.
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.
Baselines
| DM Unlearning Methods | Nudity | Van Gogh | Objects |
|---|---|---|---|
| ESD (Erased Stable Diffusion) | ✅ | ✅ | ✅ |
| FMN (Forget-Me-Not) | ✅ | ✅ | ✅ |
| AC (Ablating Concepts) | ❌ | ✅ | ❌ |
| UCE (Unified Concept Editing) | ✅ | ✅ | ❌ |
| SalUn (Saliency Unlearning) | ✅ | ❌ | ✅ |
| SH (ScissorHands) | ✅ | ❌ | ✅ |
| ED (EraseDiff) | ✅ | ❌ | ✅ |
| SPM (concept-SemiPermeable Membrane) | ✅ | ✅ | ✅ |
| AdvUnlearn (Ours) | ✅ | ✅ | ✅ |
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
Runs of OPTML-Group AdvUnlearn on huggingface.co
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More Information About AdvUnlearn huggingface.co Model
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Other API from OPTML-Group
Updated:November 21 2025
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