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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)

Pretrained DistilDIRE Checkpoints

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-2021/512x512_diffusion.pt

or you can use the following script to download the model:

wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_diffusion.pt -O models/512x512-adm.pt

Data Preparation

Before training the model on your own dataset, you need to prepare the dataset in the following format:

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 compute_dire_eps.sh

After running the script, you will have the following directory structure:

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.

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:

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 for detailed legal terms.

Acknowledgments

Our code is developed based on DIRE, guided-diffusion and 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}
}