PyDeepFakeDet is an integrated and scalable tool for Deepfake detection.

Overview

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:

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}
}
Owner
Junke, Wang
I'm a first-year Ph.D. student in the school of computer science at Fudan University, supervised by Prof. Zuxuan Wu and Prof. Yu-Gang Jiang.
Junke, Wang
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