💡 We also provide [中文文档 / CHINESE DOC]. We very welcome and appreciate your contributions to this project.

• [2024.09.27] 🔥 We officially released the initial version of Deepfake defenders, and we won the third prize in the deepfake challenge at [the conference on the bund].

sudo docker build  -t vision-rush-image:1.0.1 --network host .

sudo docker run -d --name  vision_rush_image  --gpus=all  --net host  vision-rush-image:1.0.1

Place the training-set (*.txt) file, validation-set (*.txt) file, and label (*.txt) file required for training in the dataset folder and name them with the same file name (there are various txt examples under dataset)

For the two models (RepLKNet and ConvNeXt) used, the following parameters need to be changed in main_train.py:

# For RepLKNet.
cfg.network.name = 'replknet'; cfg.train.batch_size = 16
# For ConvNeXt.
cfg.network.name = 'convnext'; cfg.train.batch_size = 24

bash main.sh

Replace the ConvNeXt model path and the RepLKNet model path in merge.py, and execute python merge.py to obtain the final inference test model.

The following example uses the POST request interface to request the image path as the request parameter, and the response output is the deepfake score predicted by the model.

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import requests
import json
import requests
import json

header = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36'
}

url = 'http://ip:10005/inter_api'
image_path = './dataset/val_dataset/51aa9b8d0da890cd1d0c5029e3d89e3c.jpg'
data_map = {'img_path':image_path}
response = requests.post(url, data=json.dumps(data_map), headers=header)
content = response.content
print(json.loads(content))