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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

1. create conda environment with Python=3.7

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

2. 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
   

3. 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

1. extract frames from videos (with ffmpeg)
2. 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
   

3. 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)

1. 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

Learned result on 4D consistency volume