DeepFake Video Detection Details 

Detects whether a video is real  or deepfake using Transformers

Project Description 

This project focuses on detecting deepfake videos using deep learning techniques. A neural network model is trained using TensorFlow and Keras to analyze video frames and identify manipulated or synthetic content. The trained model is integrated into a Flask-based web application where users can upload videos and receive prediction results. The system demonstrates how deep learning can be applied to address real-world problems related to digital media authenticity.

Features 

• Deep learning–based detection of deepfake videos

• Frame-level analysis for improved accuracy

• Trained model using TensorFlow and Keras

• Web application for video upload and prediction

• User-friendly interface built with HTML and JavaScript

• Efficient backend processing using Flask

• Well-documented and structured project code

• Suitable for academic and final-year projects

Tech Stack

• Python

• TensorFlow

• Keras

• Deep Learning (Neural Networks)

• Flask (Backend)

• HTML, JavaScript (Frontend)

• NumPy, OpenCV, and other supporting libraries

System Requirements

• Python 3.10+

• Anaconda

• Minimum 8 GB RAM 

• GPU is optional

Deliverables 

• Jupyter Notebook

• Dataset

• Setup over video call 

• WhatsApp support