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DFGC2022 Detection Solution

This repo provides an solution for the DeepFake Game Competition (DFGC) @ IJCB 2022 Detection track. Our solution achieve the 1st in the final phase of the DFGC Detection track. The ranking can be seen here

1. Authors

Institution: Shenzhen Key Laboratory of Media Information Content Security(MICS)

Adviser: Professor Bin Li

Username: HanChen

Team members:

• Han Chen
• Baoying Chen
• Linhui Hu
• Qiushi Li
• Yuzhen Lin

2. A brief report

• Model structure：ConvNext（convnext_xlarge_384_in22ft1k）and SwinTransformer(swin_large_patch4_window12_384_in22k), with weights pretrained on ImageNet dataset。

• Ensemble methods：Two ConvNext at different epochs and one Swin-Transformer.

• Augmentation methods：HorizontalFlip、GaussNoise、GaussianBlur

• Data processing：A face detector MTCNN is used to crop the face images from video frame (enlarged the face region by a factor of 1.3). Resize the input shape to (3,384,384).

• Training losses：BCELoss

• The dataset used for training：FF++(c23 and c40)[1]/(DeepFake、Face2Face、FaceSwap、FaceShifter、NeuralTextures)、FF++(c23)/HifiFace[2]、UADFV[3]、DF-TIMIT[4]、DeeperForensics-1.0[5]、DeepFakeDection(c23 and c40)[6]、Celeb-DF[7]、WildDeepfake[8]、DFDC[9]

• **Tricks **：Don't use smaller size input、Early stop training、Larger model

• References and open-source resources.

  [1] FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV 2019

  [2] https://johann.wang/HifiFace/

  [3] In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking. WIFS 2018.

  [4] Vulnerability Assessment and Detection of Deepfake Videos. ICB 2019

  [5] DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

  [6] https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html

  [7] Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. CVPR2020.

  [8] WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection. 2020 ACM MM.

  [9] The DeepFake Detection Challenge (DFDC) Dataset. Arxiv 2020.

3. Training Code

3.1 Extract faces from video and save as png (Only used to extract faces from the test set video of the competition)

python extract_video_mtcnn.py --input_root_path <video_path> --output_root_path <saved_image_path> --gpu_id <GPU_ID>

3.2 Training

## train swin_large_patch4_window12_384_in22k
python train_swin.py --model_name swin_large_patch4_window12_384_in22k --resolution 384 --gpu_id <GPU_ID>

## train convnext_xlarge_384_in22ft1k
CUDA_VISIBLE_DEVICES=0,1,2,3 python train_convnext.py --model_name convnext_xlarge_384_in22ft1k --resolution 384

3.3 Testing

## test the model weight of the 40th epoch of swin_large_patch4_window12_384_in22k
python predict_swin.py --model_name swin_large_patch4_window12_384_in22k --resolution 384 --pre_trained ./save_result/models/swin_large_patch4_window12_384_in22k_40.pth --root_path <saved_image_path> --output_txt ./save_result/pred_swin_large_patch4_window12_384_in22k_40e.txt --gpu_id <GPU_ID>

## test the model weight of the 10th epoch of convnext_xlarge_384_in22ft1k
python predict_convnext.py --model_name convnext_xlarge_384_in22ft1k --resolution 384 --pre_trained ./save_result/models/convnext_xlarge_384_in22ft1k_10.pth --root_path <saved_image_path> --output_txt ./save_result/pred_convnext_xlarge_384_in22ft1k_10e.txt --gpu_id <GPU_ID>

## test the model weight of the 30th epoch of convnext_xlarge_384_in22ft1k
python predict_convnext.py --model_name convnext_xlarge_384_in22ft1k --resolution 384 --pre_trained ./save_result/models/convnext_xlarge_384_in22ft1k_30.pth --root_path <saved_image_path> --output_txt ./save_result/pred_convnext_xlarge_384_in22ft1k_30e.txt --gpu_id <GPU_ID>

3.4 ensemble

## ensemble
python merge_csv.py --output_txt preds.txt

4. Environment

facenet-pytorch==2.5.0
torch==1.9.0
dlib==19.21.1