| | --- |
| | 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 |
| | 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> |
| | |
| |
|
| | ## Baselines |
| |
|
| | | DM Unlearning Methods | Nudity | Van Gogh | Objects | |
| | |:-------|:----:|:-------:| :-------:| |
| | | [ESD](https://github.com/rohitgandikota/erasing) (Erased Stable Diffusion) | β
| β
| β
|
| | | [FMN](https://github.com/SHI-Labs/Forget-Me-Not) (Forget-Me-Not) | β
| β
| β
|
| | | [AC](https://github.com/nupurkmr9/concept-ablation) (Ablating Concepts) | β | β
| β |
| | | [UCE](https://github.com/rohitgandikota/unified-concept-editing) (Unified Concept Editing) | β
| β
| β |
| | | [SalUn](https://github.com/OPTML-Group/Unlearn-Saliency) (Saliency Unlearning) | β
| β | β
|
| | | [SH](https://github.com/JingWu321/Scissorhands_ex) (ScissorHands) | β
| β | β
|
| | | [ED](https://github.com/JingWu321/EraseDiff) (EraseDiff) | β
| β | β
|
| | | [SPM](https://github.com/Con6924/SPM) (concept-SemiPermeable Membrane) | β
| β
| β
|
| | | **AdvUnlearn (Ours)** | β
| β
| β
|
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| | <br> |
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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} |
| | } |
| | ``` |
| |
|
| | --- |
| | license: cc-by-4.0 |
| | --- |