# Distil-DIRE
Distil-DIRE is a lightweight version of DIRE, which can be used for real-time applications. Instead of calculating DIRE image directly, Distl-DIRE aims to reconstruct the features of corresponding DIRE image forwared by a image-net pretrained classifier with one-step noise of DDIM inversion. ([Paper Link](https://arxiv.org/abs/2406.00856))
![overview](distil.png)

### Pretrained DistilDIRE Checkpoints
| Dataset | Model | Link |
| --- | --- | --- |
| CelebA-HQ | 224x224 | [link](./celebahq-distil-dire-34e.pth) |
| ImageNet | 224x224 | [link](./imagenet-distil-dire-11e.pth) |




## Pretrained ADM diffusion model
We use image-net pretrained unconditional ADM diffusion model for feature reconstruction. You can download the pretrained model from the following link:
https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt

or you can use the following script to download the model:
```bash
wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt -O models/256x256-adm.pt
```

## Data Preparation
Before training the model on your own dataset, you need to prepare the dataset in the following format:
```bash
mydataset/train|val|test
└── images
    ├── fakes
    │   └──img1.png...
    ├── reals
        └──rimg1.png...
```

After preparing the dataset, you can calculate the epsilons and dire images for the dataset using the following script:
```bash
bash compute_dire_eps.sh
```

After running the script, you will have the following directory structure:
```bash
mydataset/train|val|test
└── images
    ├── fakes
    │   └──img1.png...
    ├── reals
        └──rimg1.png...
└── eps
    ├── fakes
    │   └──img1.pt...
    ├── reals
        └──rimg1.pt...
└── dire
    ├── fakes
    │   └──img1.png...
    ├── reals
        └──rimg1.png...
``` 
For eps and dire calculation we set the DDIM steps to 20. This should be same when inference.

### Train
For training Distil-DIRE, be sure to have `datasets` directory in the root of the project and your dataset inside the `datasets` directory. 
```
torchrun --standalone --nproc_per_node 8 -m train --batch 128 --exp_name truemedia-distil-dire-mydataset --datasets mydataset --datasets_test mytestset --epoch 100 --lr 1e-4

```

#### Fine-tuning
You can also fine-tune the model on your own dataset. For fine-tuning, you need to provide the path to the pretrained model. 
```bash
torchrun --standalone --nproc_per_node 8 -m train --batch 128 --exp_name truemedia-distil-dire-mydataset --datasets mydataset --datasets_test mytestset --epoch 100 --lr 1e-4 --pretrained_weights YOUR_PRETRAINED_MODEL_PATH
```
 

### Test
For testing the model, you can use the following script:
```bash
python3 -m test --test True --datasets mydataset --pretrained_weights YOUR_PRETRAINED_MODEL_PATH
```


### with Docker 
```
export DOCKER_REGISTRY="YOUR_NAME" # Put your Docker Hub username here  
export DATE=`date +%Y%m%d` # Get the current date

# Build the Docker image for development
docker build -t "$DOCKER_REGISTRY/distil-dire:dev-$DATE" -f Dockerfile .


# Push your docker image to docker hub
docker login
docker push "$DOCKER_REGISTRY/distil-dire:dev-$DATE"

```


# Devl env 
```
export WORKDIR="YOUR_WORKDIR" # Put your working directory here
docker run --gpus=all --name=truemedia_gpu_all_distildire -v "$WORKDIR:/workspace/" -ti -e  "$DOCKER_REGISTRY/distil-dire:dev-$DATE"

# work inside the container (/workspace)
```

### Note
* This repo runs on ADM diffusion model (256x256, unconditional) trained on ImageNet 1k dataset and ResNet-50 classifier trained on ImageNet 1k dataset. 
* Minimum requirements: 1 GPU, 10GB VRAM


## Licenses
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 license.
You are free to read, share, and modify this code as long as you keep the original author attribution and non-commercial license.
Please see [this site](https://creativecommons.org/licenses/by-nc/4.0/) for detailed legal terms.

## Acknowledgments
Our code is developed based on [DIRE](https://github.com/ZhendongWang6/DIRE), [guided-diffusion](https://github.com/openai/guided-diffusion) and [CNNDetection](https://github.com/peterwang512/CNNDetection). Thanks for their sharing codes and models.

## Citation
If you find this work useful for your research, please cite our paper:
```
@misc{lim2024distildire,
      title={DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection}, 
      author={Yewon Lim and Changyeon Lee and Aerin Kim and Oren Etzioni},
      year={2024},
      eprint={2406.00856},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```