| --- |
| 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 |
| --- |
| |
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| # Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models |
| Paper can be checked in [Arxiv Preprint](https://arxiv.org/abs/2405.15234). <br> |
| Code can be checked in [GitHub](https://github.com/OPTML-Group/AdvUnlearn). <br> |
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| 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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| <div align="center"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/63e44e62789dcaae43c865d9/vad9l9ME0KD0OJKYLcFun.png" /> |
| </div> |
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| ## Baselines |
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| | 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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| ## 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} |
| } |
| ``` |
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| --- |
| license: cc-by-4.0 |
| --- |
