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[ICML 2024] Disguised Copyright Infringement of Latent Diffusion Models (arXiv)

Yiwei Lu*, Matthew Y.R. Yang*, Zuoqiu Liu*, Gautam Kamath, Yaoliang Yu

Alt text

Setup

Our code builds on, and shares requirements with Latent Diffusion Models (LDM) and Textual Inversion. To set up their environment, please run:

conda env create -f environment.yaml
conda activate ldm-copyright

You will also need the official LDM text-to-image checkpoint, available through the LDM project page.

Currently, the model can be downloaded by running:

mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt

Data

  • Symbol experiment: poison/bottle_watermark_clipped/
  • Content experiment: poison/sunflowers_clipped/
  • Style experiment: poison/style_clipped/

/base_images contains all the base images used, /target_images contains all corresponding target images used, /poison_clipped_output includes some demo results

Disguised Poison Generation

To generate disguised image from a base image to a target image, run

python create_poison.py

The following parameters can be tweaked for your experiments:

  • base_instance: base image (in e.g., png, jpg)
  • target_instance: target image
  • gpu: id of the GPU that you want to run the experiments on
  • alpha: hyperparameter that controls the tradeoff between input space constraint and feature space constraint
  • save_folder
  • load_folder
  • clipped: if set to True, bound the image between 0 and 1

For each 1000 iterations, three images will be saved:

  1. poison_$iteration.pt: your poisoned image in .pt format. This, rather than the images, should be fed into textual inversion's invert.sh.
  2. poison_$iteration.jpg: your poisoned image, displayed in .jpg format.
  3. poison_$iteration_decoded.jpg: the "revealed" disguise for encoder-decoder examination.

Be sure to define the TO_BE_DEFINED Parameters in create_poison.py. (i.e. make sure save_folder, base_image_path, target_image_path are defined properly)

Textual inversion

To invert an image set, run:

sh invert.sh

To generate new images of the learned concept, run:

sh generate.sh

You can find learned concepts in /outputs and /logs/$your_log_name/images/samples_scaled_gs....jpg.

Style

To invert an image set style, run:

sh invert_style.sh

To generate new images of the learned style, run:

sh generate_style.sh

For more details on the textual inversion process, please refer to Textual Inversion.

Appendix A: unbounded disguises

To include unbounded disguises, set loss = feature_similarity_loss in create_poison.py.

Appendix B: data augmentation

To create poisons that are robust against the horizontal flip data augmentation, set loss = feature_similarity_loss + flipped_feature_similarity_loss + noise_loss in create_poison.py.

To add in the horizontal flip data augmentation in textual inversion, uncomment image = transforms.functional.hflip(image) in ldm/data/personalized.py.

Appendix C: quantitative detection

To reproduce the quantitative detection results, run detection.ipynb after generating all symbol, style, and content poisons.

Appendix D : circumventing detection

To create poisons that circumvent detection, set loss = feature_similarity_loss + noise_loss + reconstruction_loss in create_poison.py.

Appendix E : additional experiments on disguised style

The based images and corresponding target images are provided at poison/appendix_style_clipped/. (/base_images contains all the base images used, /target_images contains all corresponding target images used, /poison_clipped_output includes some demo results)

BibTeX Citation

If you use our code in a scientific publication, we would appreciate using the following citations:

@inproceedings{LuYLKY24,
  title       ={Disguised Copyright Infringement of Latent Diffusion Model},
  author      ={Lu, Yiwei and Yang, Matthew YR and Liu, Zuoqiu and Kamath, Gautam and Yu, Yaoliang},
  booktitle   ={International Conference on Machine Learning},
  year        ={2024},
  url         ={https://arxiv.org/abs/2404.06737},
}