WOUAF

WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models (CVPR 2024)
Paper | Project page | Demo

Overview

We introduce a novel approach to model fingerprinting that assigns responsibility for the generated images, thereby serving as a potential countermeasure to model misuse. We modify generative models based on each user’s unique digital fingerprint, imprinting a unique identifier onto the resultant content that can be traced back to the user. WOUAF, incorporating finetuning into Text-to-Image (T2I) tasks using the Stable Diffusion Model, demonstrates near-perfect attribution accuracy with a minimal impact on output quality.

Requirements

Preliminary requirements:

• Python>=3.5
• PyTorch==1.12.1

Run the following command:

pip3 install -r requirements.txt

Code Usage

First, clone this repository and download the pre-extracted latent vectors for validation: Google Drive. Then, use trainval_WOUAF.py to train and evaluate the model:

CUDA_VISIBLE_DEVICES=0 python trainval_WOUAF.py \
    --pretrained_model_name_or_path stabilityai/stable-diffusion-2-base \
    --dataset_name HuggingFaceM4/COCO \
    --dataset_config_name 2014_captions --caption_column sentences_raw \
    --center_crop --random_flip \
    --dataloader_num_workers 4 \
    --train_steps_per_epoch 1_000 \
    --max_train_steps 50_000 \
    --pre_latents latents/HuggingFaceM4/COCO

Note

• Our code builds on the fine-tuning scripts for Stable Diffusion from Diffusers.
• The implementation of MappingNetwork is from StyleGAN2-ADA.

Citation

@inproceedings{kim2024wouaf,
  title={WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models},
  author={Kim, Changhoon and Min, Kyle and Patel, Maitreya and Cheng, Sheng and Yang, Yezhou},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}