Training code for ICME 2024 paper Counterfactual Explanations for Face Forgery Detection Via Adversarial Removal of Artifacts.
- clone the repository:
git clone https://github.com/yangli-lab/Artifact-Eraser
cd Artifact-Eraser
conda create -n AR python=3.7
conda activate AR
pip install -r requirements.txt
Please download the pretrained models from the following links and save them in the 'Save_Folder'
| Path | Save_Folder | Description |
|---|---|---|
| E4E Encoder | ./pretrained_models/ | Encoder4editing model pretrained by omertov. |
| StyleGAN | ./pretrained_models/ | StyleGAN model pretrained on FFHQ taken from rosinality with 1024x1024 output resolution. |
| IR-SE50 Model | ./pretrained_models/ | Pretrained IR-SE50 model taken from TreB1eN for use in ID loss during training. |
| MAT | ./classifiers/multiple_attention/pretrained/ | Pretrained deepfake detection model, MAT, by yoctta. |
| RECCE | ./classifiers/RECCE/pretrained_models/ | Pretrained deepfake detection model, RECCE, by VISION-SJTU. |
The training code of E4E encoder is borrowed from their official repository. To train the e4e encoder, make sure the paths to the required models, and prepare your dataset.
CUDA_VISIBLE_DEVICES=0 python ./scripts/train_finetune.py --dataset_type ffhq_encode \
--exp_dir ./exp/celebdf_decoder \
--start_from_latent_avg \
--use_w_pool \
--w_discriminator_lambda 0 \
--progressive_start 0 \
--train_encoder 0 \
--train_decoder 1 \
--lpips_lambda 1.0 \
--id_lambda 0.1 \
--l2_lambda 1.0 \
--val_interval 1000 \
--max_steps 1000 \
--image_interval 100 \
--board_interval 100 \
--stylegan_size 1024 \
--save_interval 500 \
--checkpoint_path /path/to/pretrained/e4e_model \
--workers 4 \
--batch_size 8 \
--test_batch_size 2 \
--test_workers 4 You can use your pretrained deepfake classifier model or train a new one with the provided code, please refer to the classifiers folder.
Let's take training MAT as an example
CUDA_VISIBLE_DEVICES=0 python3 ./classifiers/train_mat.py \
--dataset_name ffpp \
--root_dir ffpp_path \
--batch_size 32 \
--read_method frame_by_frame \
--lr 1e-3 \
--momentum 0.9 \
--weight_decay 1e-6 \
--max_iteration 1200 \
--max_warmup_iteration 0 \
--epochs 1000 \
--num_workers 2 \
--snapshot_frequency 100 \
--snapshot_template snapshot \
--exp_root save_folder \Make sure you have specified a pretrained e4e model by --checkpoint_path and pretrained deepfake classifier by --classifier_ckpt, and run the code bellow. You can specify the attack strength and attack times by setting parameters ALPHA and EPOCH.
DATASET=celebdf
CLASSIFIER=efficient
ATTACK_METHOD=fgsm
EPOCH=50
FRAME=14
ALPHA=0.0002
NAME=attack_latent_nc_${DATASET}_${CLASSIFIER}_${ATTACK_METHOD}_${EPOCH}_${FRAME}_${ALPHA}
CUDA_VISIBLE_DEVICES=0 python3 ./attack/attack_celebdf.py \
--dataset ${DATASET} \
--stylegan_size 1024 \
--encoder_type Encoder4Editing \
--device cuda:0 \
--start_from_latent_avg True \
--classifier_ckpt classifier_path \
--classifier ${CLASSIFIER} \
--attack_method ${ATTACK_METHOD} \
--mask full \
--beta 0.02 \
--alpha ${ALPHA} \
--epoch ${EPOCH} \
--frames ${FRAME} \
--loss_type mse_loss \
--lpips_lambda 0 \
--lpips_type alex \
--id_lambda 0 \
--mse_lambda 0 \
--dataset_dir data_dir \
--label_file label_for_dfdc_dataset \
--video_level False \
--attack_num 50 \
--log_dir log_path/${NAME} \
2>&1 | tee log_path/${NAME}.log \This code borrows heavily from e4e_encoder.
If you find our work interesting, please feel free to cite our paper:
put the citation here
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