Comments (2)
https://arxiv.org/pdf/2312.00084.pdf
A recent work on the robustness of data protection methods like Anti-Dreambooth.
Based on their results, Diffupure works quiet well in removing the noise
from anti-dreambooth.
Thanks, it seems that in the era of large models, it is indeed very tough to achieve robust privacy preservation.
from anti-dreambooth.
Related Issues (20)
- What happens when just run it through a noise filter like this? HOT 1
- Bugs. HOT 2
- Questions about full split sets of each dataset. HOT 2
- Hello,I have a question HOT 2
- Questions about quantitative evaluation metrics HOT 1
- ASPL script fails training on SD 1.4. HOT 6
- fail to generate adversial samples when running attack_aspl script HOT 2
- how to evaluate with FDFR and ISM
- ser_fiq.py mxnet load error HOT 1
- can you provide some perturbed images to test? HOT 2
- About the entire data for training
- TypeError: __init__() got an unexpected keyword argument 'debug'. HOT 1
- ValueError: value should be one of int, float, str, bool, or torch.Tensor
- Failed to train and output noise-ckpt in Google Colab HOT 2
- can it be used on specific pictures? HOT 6
- could not find version of module HOT 9
- Add Exif information or another indicator that technology is used to try and prevent AI training using this image. HOT 2
- Robusness under image filter(Guassian blur) HOT 1
- LDSR, ESRGAN_4x,... ?
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from anti-dreambooth.