An example of Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations (to be presented at the IJCAI-ECAI 2022). This repository contains code for crafting perceptual-aware perturbation in the Lab color space to attack an image-to-image translation network.
CelebA Dataset
bash download.sh celeba
StarGAN Model
bash download.sh pretrained-celeba-256x256
More information about the CelebA dataset can be found here.
Here is a simple example of testing our method to attack StarGAN on the CelebA dataset.
# Test
python main.py --mode test --image_size 256 --c_dim 5 --selected_attrs Black_Hair Blond_Hair Brown_Hair Male Young --model_save_dir='stargan_celeba_256/models' --result_dir='./results' --test_iters 200000 --attack_iters 100 --batch_size 1
We use some code from the original Disrupting-Deepfakes, which does a good work.
If you find this work useful, please cite our paper:
@article{wang2022anti,
title={Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations},
author={Wang, Run and Huang, Ziheng and Chen, Zhikai and Liu, Li and Chen, Jing and Wang, Lina},
journal={arXiv preprint arXiv:2206.00477},
year={2022}
}
The IJCAI camera-ready version (pdf) is available here
