Dot

<div align="center"> <h1> the Deepfake Offensive Toolkit </h1>

<a href="https://colab.research.google.com/github/sensity-ai/dot/blob/main/notebooks/colab_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=20></a>

</div>

dot (aka Deepfake Offensive Toolkit) makes real-time, controllable deepfakes ready for virtual cameras injection. dot is created for performing penetration testing against e.g. identity verification and video conferencing systems, for the use by security analysts, Red Team members, and biometrics researchers.

If you want to learn more about dot is used for penetration tests with deepfakes in the industry, read these articles by The Verge and Biometric Update.

dot is developed for research and demonstration purposes. As an end user, you have the responsibility to obey all applicable laws when using this program. Authors and contributing developers assume no liability and are not responsible for any misuse or damage caused by the use of this program.

<p align="center"> <img src="./assets/dot_intro.gif" width="500"/> </p>

How it works

In a nutshell, dot works like this

flowchart LR;
    A(your webcam feed) --> B(suite of realtime deepfakes);
    B(suite of realtime deepfakes) --> C(virtual camera injection);

All deepfakes supported by dot do not require additional training. They can be used in real-time on the fly on a photo that becomes the target of face impersonation. Supported methods:

• face swap (via SimSwap), at resolutions 224 and 512
  • with the option of face superresolution (via GPen) at resolutions 256 and 512
• lower quality face swap (via OpenCV)
• FOMM, First Order Motion Model for image animation

Running dot

Graphical interface

GUI Installation

Download and run the dot executable for your OS:

• Windows (Tested on Windows 10 and 11):

  • Download dot.zip from here, unzip it and then run dot.exe

• Ubuntu:

  • ToDo

• Mac (Tested on Apple M2 Sonoma 14.0):

  • Download dot-m2.zip from here and unzip it
  • Open terminal and run xattr -cr dot-executable.app to remove any extended attributes
  • In case of camera reading error:
    • Right click and choose Show Package Contents
    • Execute dot-executable from Contents/MacOS folder

GUI Usage

Usage example:

1. Specify the source image in the field source.
2. Specify the camera id number in the field target. In most cases, 0 is the correct camera id.
3. Specify the config file in the field config_file. Select a default configuration from the dropdown list or use a custom file.
4. (Optional) Check the field use_gpu to use the GPU.
5. Click on the RUN button to start the deepfake.

For more information about each field, click on the menu Help/Usage.

Watch the following demo video for better understanding of the interface

<p align="center"> <img src="./assets/gui_dot_demo.gif" width="500" height="406"/> </p>

Command Line

CLI Installation

Install Pre-requisites

• Linux

  sudo apt install ffmpeg cmake
  

• MacOS

  brew install ffmpeg cmake
  

• Windows

  1. Download and install Visual Studio Community from here
  2. Install Desktop development with C++ from the Visual studio installer

Create Conda Environment

The instructions assumes that you have Miniconda installed on your machine. If you don't, you can refer to this link for installation instructions.

With GPU Support

conda env create -f envs/environment-gpu.yaml
conda activate dot

Install the torch and torchvision dependencies based on the CUDA version installed on your machine:

• Install CUDA 11.8 from link

• Install cudatoolkit from conda: conda install cudatoolkit=<cuda_version_no> (replace <cuda_version_no> with the version on your machine)

• Install torch and torchvision dependencies: pip install torch==2.0.1+<cuda_tag> torchvision==0.15.2+<cuda_tag> torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118, where <cuda_tag> is the CUDA tag defined by Pytorch. For example, pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118 for CUDA 11.8.

  Note: torch1.9.0+cu111 can also be used.

To check that torch and torchvision are installed correctly, run the following command: python -c "import torch; print(torch.cuda.is_available())". If the output is True, the dependencies are installed with CUDA support.

With MPS Support(Apple Silicon)

conda env create -f envs/environment-apple-m2.yaml
conda activate dot

To check that torch and torchvision are installed correctly, run the following command: python -c "import torch; print(torch.backends.mps.is_available())". If the output is True, the dependencies are installed with Metal programming framework support.

With CPU Support (slow, not recommended)

conda env create -f envs/environment-cpu.yaml
conda activate dot

Install dot

pip install -e .

Download Models

• Download dot model checkpoints from here
• Unzip the downloaded file in the root of this project

CLI Usage

Run dot --help to get a full list of available options.

1. Simswap

   dot -c ./configs/simswap.yaml --target 0 --source "./data" --use_gpu
   

2. SimSwapHQ

   dot -c ./configs/simswaphq.yaml --target 0 --source "./data" --use_gpu
   

3. FOMM

   dot -c ./configs/fomm.yaml --target 0 --source "./data" --use_gpu
   

4. FaceSwap CV2

   dot -c ./configs/faceswap_cv2.yaml --target 0 --source "./data" --use_gpu
   
   

Note: To enable face superresolution, use the flag --gpen_type gpen_256 or --gpen_type gpen_512. To use dot on CPU (not recommended), do not pass the --use_gpu flag.

Controlling dot with CLI

Disclaimer: We use the SimSwap technique for the following demonstration

Running dot via any of the above methods generates real-time Deepfake on the input video feed using source images from the data/ folder.

<p align="center"> <img src="./assets/dot_run.gif" width="500"/> </p>

When running dot a list of available control options appear on the terminal window as shown above. You can toggle through and select different source images by pressing the associated control key.

Watch the following demo video for better understanding of the control options:

<p align="center"> <img src="./assets/dot_demo.gif" width="480"/> </p>

Docker

Setting up docker

• Build the container

  docker-compose up --build -d
  

• Access the container

  docker-compose exec dot "/bin/bash"
  

Connect docker to the webcam

Ubuntu

1. Build the container

   docker build -t dot -f Dockerfile .
   

2. Run the container

   xhost +
   docker run -ti --gpus all \
   -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
   -e NVIDIA_VISIBLE_DEVICES=all \
   -e PYTHONUNBUFFERED=1 \
   -e DISPLAY \
   -v .:/dot \
   -v /tmp/.X11-unix:/tmp/.X11-unix:rw \
   --runtime nvidia \
   --entrypoint /bin/bash \
   -p 8080:8080 \
   --device=/dev/video0:/dev/video0 \
   dot
   

Windows

1. Follow the instructions here under Windows to set up the webcam with docker.

2. Build the container

   docker build -t dot -f Dockerfile .
   

3. Run the container

   docker run -ti --gpus all \
   -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
   -e NVIDIA_VISIBLE_DEVICES=all \
   -e PYTHONUNBUFFERED=1 \
   -e DISPLAY=192.168.99.1:0 \
   -v .:/dot \
   --runtime nvidia \
   --entrypoint /bin/bash \
   -p 8080:8080 \
   --device=/dev/video0:/dev/video0 \
   -v /tmp/.X11-unix:/tmp/.X11-unix \
   dot
   

macOS

1. Follow the instructions here to set up the webcam with docker.

2. Build the container

   docker build -t dot -f Dockerfile .
   

3. Run the container

   docker run -ti --gpus all \
   -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
   -e NVIDIA_VISIBLE_DEVICES=all \
   -e PYTHONUNBUFFERED=1 \
   -e DISPLAY=$IP:0 \
   -v .:/dot \
   -v /tmp/.X11-unix:/tmp/.X11-unix \
   --runtime nvidia \
   --entrypoint /bin/bash \