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sensity-ai/dot

4,547PythonAudience · ops devopsComplexity · 4/5LicenseSetup · hard

TLDR

dot (Deepfake Offensive Toolkit) is a Python tool for security researchers that swaps your face with a target photo in real time via webcam, routing the result to a virtual camera to test whether identity verification systems can be fooled.

Mindmap

mindmap
  root((dot))
    What it does
      Real-time face swap
      Virtual camera output
      No custom training needed
    Swap methods
      SimSwap high quality
      OpenCV lightweight
      First Order Motion Model
    Run modes
      Desktop GUI app
      Command-line interface
    Best for
      Security researchers
      Red team identity testing
      Liveness detection audits
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Things people build with this

USE CASE 1

Test whether a video-call platform or biometric onboarding service can detect a real-time face-swap using SimSwap

USE CASE 2

Red-team an identity verification system by routing a deepfake webcam feed through a virtual camera device

USE CASE 3

Evaluate the robustness of liveness-detection checks in a KYC flow by presenting a face-swapped video stream

USE CASE 4

Research First Order Motion Model face animation by animating a still photo to mimic head movements in real time

Tech stack

PythonCondaOpenCVSimSwapPyTorch

Getting it running

Difficulty · hard Time to first run · 1h+

Requires Python, Conda, and manual download of pre-trained model files, GPU is strongly recommended for real-time performance and CLI setup is non-trivial.

Free to use, modify, and distribute for any purpose, including commercial, as long as the BSD 3-Clause copyright notice is retained.

In plain English

dot, short for Deepfake Offensive Toolkit, is a Python tool that generates real-time face-swap deepfakes from a webcam feed and routes the result into a virtual camera. The intended audience is security researchers and red team professionals who test whether identity verification systems, video call platforms, or biometric onboarding services can be fooled by a digitally altered face. The README notes that users are responsible for complying with local laws, and that the authors take no responsibility for misuse. The core idea is that you give the tool a still photo of a target face, point it at your webcam, and it replaces your face with the target face in real time. The output is then available as a virtual camera device, so any app that accepts a webcam as input will see the swapped face instead of your real one. No additional training is required on your end: the models ship pre-trained and work immediately once downloaded. Several face-swap techniques are bundled. The highest quality option uses a method called SimSwap at two resolution settings, with an optional enhancement step for sharpening the result. A lighter-weight option uses the OpenCV library for lower-fidelity swaps. A third approach, based on a model called First Order Motion Model, animates a still photo to mimic head and facial movements rather than doing a direct pixel swap. You can use dot through a graphical desktop application available as a downloadable executable for Windows and Mac, or through a command-line interface that requires more setup including Python, Conda, and pre-trained model files. GPU support is available and recommended for performance, though CPU-only mode works at reduced speed. The project is built by Sensity AI and is licensed under the BSD 3-Clause license. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
I am a security researcher testing a KYC onboarding flow. Walk me through setting up dot on a Mac with a GPU, downloading the SimSwap model files, and routing the virtual camera into a browser-based video call.
Prompt 2
How do I use dot's command-line interface to run a SimSwap face swap at high resolution with the optional sharpening enhancement enabled?
Prompt 3
I want to test how well a video-conferencing app's background-blur detects deepfakes. How do I configure dot to output to a virtual camera that the app picks up?
Prompt 4
What is the difference between the SimSwap, OpenCV, and First Order Motion Model face-swap modes in dot? When would I choose each one for a red-team test?
Prompt 5
I only have a CPU, no GPU. Can dot still run the face swap? What frame rate should I expect and which mode is fastest on CPU?
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