Deep fake detection Django Application

This projects aims in detection of video deepfakes using deep learning techniques like ResNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained ResNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features.

Requirements:

Note : Nvidia GPU is mandatory to run the application.

• CUDA version >= 10.0 for GPU
• GPU Compute Capability > 3.0

Main requirements are listed below:

Python >= v3.6
Django >= v3.0

Directory Structure

• ml_app -> Directory containing code in views.py file
• project_settings -> Contains Django settings and files to run in production
• static -> Contains all css, js and json files (for face-api)
• templates -> Template files for HTML

Note: Before running the project make sure you have created directories namely models, uploaded_images, uploaded_videos in the project root and that you have proper permissions to access them.

Prerequisite

Step 1: Create virtualenv (optional)

python -m venv venv

Step 2: Activate virtualenv (optional)

venv\Scripts\activate

Step 3: Install requirements

pip install -r requirements.txt

Step 4: Copy Models

Copy your trained model to the models folder i.e Django Application/models/

Step 5: Run project

python manage.py runserver