Updated: Aug 23, 2023
1-Click Kit 
Deepfake detection is identifying manipulated or synthetic media content using machine learning algorithms and computer vision techniques. It detects 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
Deployment Information
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
Development Environment
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.
jupyterby jupyter
14404 Version:CurrentJupyter metapackage for installation, docs and chat
jupyterby jupyter
Python 14404 Version:Current License: Permissive (BSD-3-Clause)
Machine Learning
Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning
facenet-pytorchby timesler
3538 Version:v2.5.3Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
facenet-pytorchby timesler
Python 3538 Version:v2.5.3 License: Permissive (MIT)
pytorchby pytorch
67874 Version:v2.0.1Tensors and Dynamic neural networks in Python with strong GPU acceleration
pytorchby pytorch
Python 67874 Version:v2.0.1 License: Others (Non-SPDX)
Kit Solution Source
deepfake-detectionby kandi1clickkits
0 Version:v1.0.0Identify the images as real or fake using state-of-the-art AI models
deepfake-detectionby kandi1clickkits
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)
API Integration
gradioby gradio-app
18771 Version:v3.35.2Create UIs for your machine learning model in Python in 3 minutes
gradioby gradio-app
Python 18771 Version:v3.35.2 License: Permissive (Apache-2.0)