https://github.com/DariusAf/MesoNet

"MesoNet: a Compact Facial Video Forgery Detection Network" (D. Afchar, V. Nozick) - IEEE WIFS 2018
https://github.com/DariusAf/MesoNet

deepfake face2face mesonet

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"MesoNet: a Compact Facial Video Forgery Detection Network" (D. Afchar, V. Nozick) - IEEE WIFS 2018

• Host: GitHub
• URL: https://github.com/DariusAf/MesoNet
• Owner: DariusAf
• License: apache-2.0
• Created: 2018-04-27T07:30:38.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-05-07T10:04:32.000Z (8 months ago)
• Last Synced: 2024-08-01T01:27:51.078Z (5 months ago)
• Topics: deepfake, face2face, mesonet
• Language: Python
• Homepage:
• Size: 476 KB
• Stars: 245
• Watchers: 7
• Forks: 109
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# MesoNet

You can find here the implementation of the network architecture and the dataset used in our paper on digital forensics. It was accepted at the [WIFS 2018 conference](http://wifs2018.comp.polyu.edu.hk).

> We present a method to automatically detect face tampering in videos. We particularly focus on two recent approaches used to generate hyper-realistic forged videos: deepfake and face2face. Traditional image forensics techniques are usually not well suited to videos due to their compression that strongly degrades the data. Thus, we follow a deep learning approach and build two networks, both with a low number of layers to focus on mesoscopic properties of the image. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. Our tests demonstrate a successful detection for more than 98\% for deepfake and 95\% for face2face.

[Link to full paper](https://arxiv.org/abs/1809.00888)

[Demonstrastion video (light)](https://www.youtube.com/watch?v=vch1CmgX0LA)

## Requirements

- Python 3.5

- Numpy 1.14.2

- Keras 2.1.5

If you want to use the complete pipeline with the face extraction from the videos, you will also need the following librairies :

- [Imageio](https://pypi.org/project/imageio/)

- [FFMPEG](https://www.ffmpeg.org/download.html)

- [face_recognition](https://github.com/ageitgey/face_recognition)

## Dataset

### Aligned face datasets

|Set|Size of the forged image class|Size of real image class|

|---|---|---|

|Training|5111|7250|

|Validation|2889|4259|

- Training set (~150Mo)

- Validation set (~50Mo)

[Download link for the dataset](https://my.pcloud.com/publink/show?code=XZLGvd7ZI9LjgIy7iOLzXBG5RNJzGFQzhTRy)

## Pretrained models

You can find the pretrained weight in the `weights` folder. The `_DF` extension correspond to a model trained to classify deepfake-generated images and the `_F2F` to Face2Face-generated images.

## Authors

**Darius Afchar** - École des Ponts Paristech | École Normale Supérieure (France)

**Vincent Nozick** - [Website](http://www-igm.univ-mlv.fr/~vnozick/?lang=fr)

## References

Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018, September). [MesoNet: a Compact Facial Video Forgery Detection Network](https://arxiv.org/abs/1809.00888). In IEEE Workshop on Information Forensics and Security, WIFS 2018.

This research was carried out while the authors stayed at the National Institute of Informatics, Japan.