PyDeepFakeDet

An integrated and scalable library for Deepfake detection research.

Introduction

PyDeepFakeDet is an integrated and scalable Deepfake detection tool developed by Fudan Vision and Learning Lab. The goal is to provide state-of-the-art Deepfake detection Models as well as interfaces for the training and evaluation of new Models on commonly used Deepfake datasets.

This repository includes implementations of both CNN-based and Transformer-based methods:

• CNN Models

  • ResNet
  • Xception
  • EfficientNet
  • MesoNet
  • GramNet
  • Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
  • Multi-attentional Deepfake Detection

• Transformer Models

  • ViT
  • M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

Model Zoo and Baselines

The baseline Models on three versions of FF-DF dataset are provided.

Method	RAW	C23	C40	Model
ResNet50	97.61	94.87	84.95	RAW / C23 / C40
Xception	97.84	95.24	86.27	RAW / C23 / C40
EfficientNet-b4	97.89	95.61	87.12	RAW / C23 / C40
Meso4	85.14	77.14	60.13	RAW / C23 / C40
MesoInception4	95.45	84.13	71.31	RAW / C23 / C40
GramNet	97.65	95.16	86.21	RAW / C23 / C40
F3Net	99.95	97.52	90.43	RAW / C23 / C40
MAT	97.90	95.59	87.06	RAW / C23 / C40
ViT	96.72	93.45	82.97	RAW / C23 / C40
M2TR	99.50	97.93	92.89	RAW / C23 / C40

The baseline Models on Celeb-DF is also available.

Method	Celeb-DF	Model
ResNet50	98.51	CelebDF
Xception	99.05	CelebDF
EfficientNet-b4	99.44	CelebDF
Meso4	73.04	CelebDF
MesoInception4	75.87	CelebDF
GramNet	98.67	CelebDF
F3Net	96.47	CelebDF
MAT	99.02	CelebDF
ViT	96.73	CelebDF
M2TR	99.76	CelebDF

Installation

• We use Python == 3.9.0, torch==1.11.0, torchvision==1.12.0.

• Install the required packages by:

  pip install -r requirements.txt

Data Preparation

Please follow the instructions in DATASET.md to prepare the data.

Quick Start

Specify the path of your local dataset in ./configs/resnet50.yaml, and then run:

python run.py --cfg resnet50.yaml

Visualization tools

Please refer to VISUALIZE.md for detailed instructions.

Contributors

PyDeepFakeDet is written and maintained by Wenhao Ouyang, Chao Zhang, Zhenxin Li, and Junke Wang.

License

PyDeepFakeDet is released under the MIT license.

Citations

@inproceedings{wang2021m2tr,
  title={M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection},
  author={Wang, Junke and Wu, Zuxuan and Ouyang, Wenhao and Han, Xintong and Chen, Jingjing and Lim, Ser-Nam and Jiang, Yu-Gang},
  booktitle={ICMR},
  year={2022}
}