Exposing-the-Deception

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Exposing-the-Deception

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This repo is the official implementation of “Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection”. Accepted by AAAI-2024.

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# Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
![](https://github.com/QingyuLiu/Exposing-the-Deception/blob/main/framework.png)
This repo is the official implementation of “Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection”. Accepted by AAAI-2024.

## Installation
Our code is implemented and evaluated on pytorch. The following packages are used by our code.
- `torch==2.0.1`
- `albumentations==1.3.1`
- `opencv-python==4.8.1.78`
- `scipy==1.10.1`
- `tensorboard==2.14.0`
- `numpy==1.24.3`
- `tqdm==4.66.1`

Our code is evaluated on `Python 3.8.11` and `CUDA 11.7`.

## Training
### Prepare Datasets
- Prepare face forgery datasets: [FaceForensics++](https://github.com/ondyari/FaceForensics), [Celeb-DF-V1](https://github.com/yuezunli/celeb-deepfakeforensics), [Celeb-DF-V2](https://github.com/yuezunli/celeb-deepfakeforensics), [DFDC-Preview](https://ai.meta.com/datasets/dfdc/), [DFDC](https://www.kaggle.com/c/deepfake-detection-challenge/data)
- Preprocess the video: extract frames from videos, and then extract facial images using [RetinaFace](https://github.com/biubug6/Pytorch_Retinaface). 
To train or test the model, you should provide a dataset path and label txt, which need to have the following folder structure. 
```Shell
dataset
|-- FF++ < dataset name >
    |-- train_fake.txt < data line: label,path,the number of frames\n >
    |-- train_real.txt
    |-- val_fake.txt
    |-- val_real.txt
    |-- test_fake.txt
    |-- test_real.txt
|-- Celeb-DF-V1
    |-- ...
|-- ...
```

### Train Models
You should make `output` and `logs` folders to save files of the model and log before running the following command.
```
python training.py  --name                                      \

(arguments for training)
                    --gpu_num 0,1                                                   \
                    --model resnet,efficientnet,mobilenet                           \
                    --epoch 20                                                      \
                    --weight_decay 1e-6                                             \ 
                    --lr 1e-3                                                       \
                    --bs 256                                                        \
                    --test_bs 1000                                                  \
                    --num_workers 12                                                \
                    --size 224                                                      \
                    --dataset FF++_c23,Celeb-DF-v2,Celeb-DF-v1,DFDC-Preview,DFDC    \
                    --mixup True                                                    \
                    --alpha 0.5                                                     \

(arguments for loss)
                    --lil_loss True                             \
                    --gil_loss True                             \
                    --temperature 1.5                           \
                    --mi_calculator kl                          \
                    --balance_loss_method auto,hyper            \
                    --scales [1,2,10]                           \

(model parameters)
                    --num_LIBs 4                                \
                    --resume_model output/{name}/....           \

(checkpoint)
                    --test False                                \
                    --save_model True                           \
                    --save_path output                          \
```
The following is a description of some parameters in the configuration file:
- `model`: the backbone type of LIB, which supports resnet, efficientnet, mobilenet.
- `dataset`: is the dataset name, which corresponds to `dataset/{dataset_name}` path.
- `lil_loss`, `gil_loss`: `True` is to use Local Information Loss or Gocal Information Loss proposed by our work.
- `mi_calculator`: is the algorithm to calculate the mutual information, which supports KL divergence and Wasserstein distance.
- `balance_loss_method`: is the method for determining $\alpha$ and $\beta$ in equation (13) of the paper, and supports auto and hyper. If it is hyper, `scales` is the setting of weights, which respectively represent the weights of classification loss, the local information loss, and the global information loss.
- `num_LIBs`: is the number of Local Information Block.

## Citation
```
@article{ba2024exposing,
  title={Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection},
  author={Ba, Zhongjie and Liu, Qingyu and Liu, Zhenguang and Wu, Shuang and Lin, Feng and Lu, Li and Ren, Kui},
  journal={arXiv preprint arXiv:2403.01786},
  year={2024}
}
```

Exposing-the-Deception

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