PCL-I2G

Unofficial implementation of paper: Learning Self-Consistency for Deepfake Detection (ICCV2021)

Disclaimer

This repo is still developing (training). Please open an issue or contact me if you have any questions.

Forked/Modified from chail/patch-forensics (Thanks!)

Installation

create conda environment with Python=3.7

conda create -n PCL-I2G python=3.7
conda activate PCL-I2G

install pytorch 1.9.0, torchvision 0.10.0 with compatible cuda version

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

install required packages
```
pip install -r requirements.txt
```

Dataset

Basically any real data works on the methodology, but here I use FaceForensics++.

FaceForensics++

Pre-processing

extract frames from videos (with ffmpeg)

Download dlib landmark predictor

# dlib facial landmarks predictor
wget -O resources/shape_predictor_68_face_landmarks.dat.bz2 https://github.com/davisking/dlib-models/raw/master/shape_predictor_68_face_landmarks.dat.bz2
bzip2 -d resources/shape_predictor_68_face_landmarks.dat.bz2

run data/preprocessing/faceforensics_process_frames.py

python -m data.processing.faceforensics_process_frames.py \
    --source_dir_manipulated $manipulated \
    --source_dir_original $original \
    --outsize $outsize \
    --output_dir $out\
    --split resources/splits/train.json
    # --split resources/splits/val.json
    # --split resources/splits/test.json

or run scripts/preprocess.sh

bash scripts/train.sh

Inconsistency Image Generator (I2G)

run generate_I2G.py

python generate_I2G.py \
    --real_im_path $original \
    --batch_size 512 \
    --out_size 256 \
    --output_dir $out

Pair-Wise Self-Consistency Learning (PCL)

Training

run train.py:

python train.py \
	--gpu_ids $gpu --seed 0 --loadSize 256 --fineSize 256 \
	--name PCL-I2G-FF256-32frames-Modified-5e-5 --save_epoch_freq 10 \
 	--real_im_path $dset/real/face \
 	--fake_im_path $dset/fake/face \
	--which_model_netD resnet34_layer4_extra3 \
    --model patch_inconsistency_discriminator \
	--lbda 10 --patience 5 --lr_policy constant \
    --max_epochs 500 --batch_size 512 --lr 5e-5 \
	--overwrite_config

or run train_PCL.sh

bash scrips/train_PCL.sh

Argument --which_model_netD resnet34_layer4_extra3 and --model patch_inconsistency_discriminator is the model architecture used in the paper.

Testing

run test.py

python test.py --which_epoch $which_epoch --gpu_ids $gpu --partition $partition \
    --average_mode after_softmax --topn 100 --force_redo \
    --dataset_name FF-DF \
    --real_im_path $dset/original/$partition \
    --fake_im_path $dset/DF/$partition \
    --train_config $checkpoint/opt.yml

jtchen0528/PCL-I2G