COMP 540: Statistical Machine Learning

DeepFake Detection project for the course at Rice University in Fall 2023.

Team Members:
Priyam Thakkar - pt50@rice.edu
Pranav Suryadevara - pranav.suryadevara@rice.edu

This repository contains the python jupyter notebooks built for the course project.
It also consists of a lightweight flask application that uses the final selected model.Priyam Thakkar developed for a previous course and we tweaked it to our project specifications.

Deepfake Detection Flask Application

This repository contains a Flask application that can classify videos as either original or manipulated (deepfake). The application utilizes a machine learning model to analyze video frames and generate a classification result.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.6+
• pip (Python package manager)

Installation

To set up the project environment, follow these steps:

1. Clone the repository to your local machine:

git clone https://github.com/suryadevarapranav/Statistical-ML.git

2. Navigate to the project directory:

cd 'Deepfake Project'

3. Install the required Python packages:

pip install -r requirements.txt

Running the Application

To run the Flask application, use the following command:

python3 app.py

After running the command, you can access the application at http://127.0.0.1:5000/ in your web browser.

Using the Application

To use the application:

1. Navigate to the home page at http://127.0.0.1:5000/.
2. Click on "Select file" to upload a video file.
3. Click "Upload and Analyze" to submit the file for processing.
4. The application will display the classification results and relevant heatmaps on a new results page.