Build a DeepFake Detection Engine
by kandikits Updated: Apr 10, 2023
Deepfake detection is the process of identifying manipulated or synthetic media content using machine learning algorithms and computer vision techniques to detect anomalies in facial and body movements, and other visual artifacts.
In this kit, we build a Deepfake Detection Engine using the popular Facenet_pytorch is a Python library that provides implementations of deep learning models for face recognition tasks. It includes pre-trained models such as
- MTCNN (Multi-Task Cascaded Convolutional Networks) for face detection and alignment, and
- InceptionResnetV1 for detecting whether an image is fake or real.
We use these two models to detect and recognize faces in images with high accuracy. The library is built on top of PyTorch, a popular open-source machine learning framework, and provides an easy-to-use API for face recognition tasks
For Windows OS,
- Download, extract the zip file and run. Do ensure to extract the zip file before running it.
- After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook.
- To run the kit manually, press 'N' and locate the zip file 'deepfake-detection.zip'.
- Extract the zip file and navigate to the directory 'deepfake-detection'.
- Open command prompt in the extracted directory 'deepfake-detection' and run the command 'jupyter notebook'
- Locate and open the 'Deepfake_detection.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook.
For other Operating Systems,
- Click here to download the repository.
- Extract the zip file and navigate to the directory deepfake-detection.zip
Click the button below to download the solution and follow the deployment information to begin set-up. This 1-click kit has all the required dependencies and resources to build your DeepFake Detection Engine.
Libraries used in this solution
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Notebook is used for our development.
Jupyter metapackage for installation, docs and chat
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