Deepfake Offensive Toolkit

build-dot code-check

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 this article by The Verge

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.

How it works

In a nutshell, dot works like this

    __________________      _____________________________      __________________________
   | your webcam feed | -> | suite of realtime deepfakes | -> | 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


Install Pre-requisites

  • Linux

    sudo apt install ffmpeg cmake
  • MacOS

    brew install ffmpeg cmake

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 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==1.9.0+<cuda_tag> torchvision==0.10.0+<cuda_tag> -f, where <cuda_tag> is the CUDA tag defined by Pytorch. For example, pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f for CUDA 11.1. See here for a list of all available torch and torchvision versions.

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 CPU Support (slow, not recommended)

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

Install dot

pip install -e .

Download Models

There are 2 options for downloading the model weights:

  • GDrive: Download folder from here, unzip and place the folder in the root directory of the repository.

  • gdown: Run the following command:

    gdown -O ./saved_models --folder


Running dot

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

  1. Simswap

    dot \
    --swap_type simswap \
    --target 0 \
    --source "./data" \
    --parsing_model_path ./saved_models/simswap/parsing_model/checkpoint/79999_iter.pth \
    --arcface_model_path ./saved_models/simswap/arcface_model/arcface_checkpoint.tar \
    --checkpoints_dir ./saved_models/simswap/checkpoints \
    --show_fps \
  2. SimSwapHQ

    dot \
    --swap_type simswap \
    --target 0 \
    --source "./data" \
    --parsing_model_path ./saved_models/simswap/parsing_model/checkpoint/79999_iter.pth \
    --arcface_model_path ./saved_models/simswap/arcface_model/arcface_checkpoint.tar \
    --checkpoints_dir ./saved_models/simswap/checkpoints \
    --crop_size 512 \
    --show_fps \

    Additionally, to enable face superresolution, use the flag --gpen_type gpen_256 or --gpen_type gpen_512.

  3. FOMM

    dot \
    --swap_type fomm \
    --target 0 \
    --source "./data" \
    --model_path ./saved_models/fomm/vox-adv-cpk.pth.tar \
    --show_fps \
  4. FaceSwap

    dot \
    --swap_type faceswap_cv2 \
    --target 0 \
    --source "./data" \
    --model_path ./saved_models/faceswap_cv/shape_predictor_68_face_landmarks.dat \
    --show_fps \

Note: To use dot on CPU (not recommended), do not pass the --use_gpu flag.

Controlling dot

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.

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:

Virtual Camera Injection

Instructions vary depending on your operating system.


Choose Install and register only 1 virtual camera.

  • Run OBS Studio.

  • In the Sources section, press on Add button (“+” sign),

    select Windows Capture and press OK. In the appeared window, choose “[python.exe]: fomm” in Window drop-down menu and press OK. Then select Edit -> Transform -> Fit to screen.

  • In OBS Studio, go to Tools -> VirtualCam. Check AutoStart,

    set Buffered Frames to 0 and press Start.

  • Now OBS-Camera camera should be available in Zoom

    (or other videoconferencing software).


sudo apt update
sudo apt install v4l-utils v4l2loopback-dkms v4l2loopback-utils
sudo modprobe v4l2loopback devices=1 card_label="OBS Cam" exclusive_caps=1
v4l2-ctl --list-devices
sudo add-apt-repository ppa:obsproject/obs-studio
sudo apt install obs-studio

Open OBS Studio and check if tools --> v4l2sink exists. If it doesn’t follow these instructions:

mkdir -p ~/.config/obs-studio/plugins/v4l2sink/bin/64bit/
ln -s /usr/lib/obs-plugins/ ~/.config/obs-studio/plugins/v4l2sink/bin/64bit/

Use the virtual camera with OBS Studio:

  • Open OBS Studio
  • Go to tools --> v4l2sink
  • Select /dev/video2 and YUV420
  • Click on start
  • Join a meeting and select OBS Cam


  • Download and install OBS Studio for MacOS from here
  • Open OBS and follow the first-time setup (you might be required to enable certain permissions in System Preferences)
  • Run dot with --use_cam flag to enable camera feed
  • Click the “+” button in the sources section → select “Windows Capture”, create a new source and enter “OK” → select window with “python” included in the name and enter OK
  • Click “Start Virtual Camera” button in the controls section
  • Select “OBS Cam” as default camera in the video settings of the application target of the injection


This is not a commercial Sensity product, and it is distributed freely with no warranties

The software is distributed under BSD 3-Clause. dot utilizes several open source libraries. If you use dot, make sure you agree with their licenses too. In particular, this codebase is built on top of the following research projects:


This repository follows the Google Python Style Guide for code formatting.

If you have ideas for improving dot, feel free to open relevant Issues and PRs. Please read before contributing to the repository.

If you are working on improving the speed of dot, please read first our guide on code profiling.

Setup Dev-Tools

  1. Install Dev Requirements

    pip install -r requirements-dev.txt
  2. Install Pre-Commit Hooks

    pre-commit install
  3. Run Unit Tests (with coverage)

    pytest --cov=dot --cov-report=term --cov-fail-under=10

List of maintainers



View Github