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README.md

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+# 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. 
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+
+
+## 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)**  | ✅  | ✅   |  ✅ 
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+<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
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+---