Deepfake Detection using Convolutional Neural Networks (CNNs)

The above files from 0 to 3 covers model training steps.

Note:

The is a paid project.

Project Link:

https://portfolio-six-pearl-34.vercel.app/

Overview

This repository contains a deep learning project focused on detecting deepfake images and videos using Convolutional Neural Networks (CNNs). Deepfakes are AI-generated media that manipulate visual and auditory content, posing challenges related to misinformation and privacy.

Key Features

CNN Architecture

• Utilizes state-of-the-art CNN models tailored for deepfake detection, such as ResNet, VGG, or custom architectures.

Dataset

• Includes curated datasets of both real and deepfake images/videos for training and evaluation.

Preprocessing

• Implements preprocessing techniques specific to deepfake detection:
  • Face detection and alignment.
  • Data augmentation to enhance model robustness.

Training Pipeline

• Provides scripts and notebooks for training CNN models on GPU-enabled hardware, optimizing performance.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

About

This project was developed to combat the rise of deepfake technology by providing a robust framework for detecting manipulated media using deep learning techniques. By open-sourcing this repository, we aim to empower researchers and developers to contribute to the ongoing effort in combating digital misinformation.