Awesome

Official Implementation of WEvade

This code is the official implementation of our CCS'23 paper: Evading Watermark based Detection of AI-Generated Content Paper.

Preparation

1. Clone this repo from the GitHub.

    git clone https://github.com/zhengyuan-jiang/WEvade.git
   

2. Setup environment.

• Install the pytorch. The latest codes are tested on Ubuntu 16.04, PyTorch 1.x.x and Python 3.x:

• Install the adversarial-robustness-toolbox:

  pip install adversarial-robustness-toolbox
  

Run WEvade-W (white-box attack)

Run WEvade-W-II:

## Standard training
python3 main.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val'

## Adversarial training
python3 main.py --checkpoint './ckpt/coco_adv_train.pth' --dataset-folder './dataset/coco/val'

Run other variants:

## WEvade-W-I
python3 main.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --WEvade-type 'WEvade-W-I'

## Single-tailed detector
python3 main.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --detector-type 'single-tailed'

## Binary search
python3 main.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --binary-search True

Run existing post-processing methods:

## Standard training
python3 existing_post_processing.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val'

## Adversarial training
python3 existing_post_processing.py --checkpoint './ckpt/coco_adv_train.pth' --dataset-folder './dataset/coco/val'

Run WEvade-B-Q (query based black-box attack)

1. Encode watermark and save watermarked images (watermark is generated using the random seed):

    ## Standard training
    python3 encode_watermarked_images.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --exp 'COCO'
   
    ## Adversarial training
    python3 encode_watermarked_images.py --checkpoint './ckpt/coco_adv_train.pth' --dataset-folder './dataset/coco/val' --exp 'COCO-ADV'
   

2. (Optional) Decode watermarked images for evaluation:

    ## Standard training
    python3 decode_watermarked_images.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --exp 'COCO'
   
    ## Adversarial training
    python3 decode_watermarked_images.py --checkpoint './ckpt/coco_adv_train.pth' --dataset-folder './dataset/coco/val' --exp 'COCO-ADV'
   

3. Run WEvade-B-Q:

    ## Standard training
    python3 main_WEvade_B_Q.py --checkpoint './ckpt/coco.pth' --dataset-folder './dataset/coco/val' --exp 'COCO' --num-attack 10 --norm 'inf'
    
    ## Adversarial training
    python3 main_WEvade_B_Q.py --checkpoint './ckpt/coco_adv_train.pth' --dataset-folder './dataset/coco/val' --exp 'COCO-ADV' --num-attack 10 --norm 'inf'
   

Citation

If you find our work useful for your research, please consider citing the paper

@inproceedings{jiang2023evading,
  title={Evading Watermark based Detection of AI-Generated Content},
  author={Jiang, Zhengyuan and Zhang, Jinghuai and Gong, Neil Zhenqiang},
  booktitle={ACM Conference on Computer and Communications Security (CCS)},
  year={2023}
}