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Deepfake Detection System 🚀

Welcome to the Deepfake Detection System project! This project aims to detect deepfake videos using advanced machine learning techniques. The model leverages pre-trained CNNs for feature extraction and LSTM networks for temporal modeling, achieving high accuracy in distinguishing real and fake videos.

Deepfake Detection System Screenshot

Table of Contents

Introduction 📖

Deepfakes are synthetic media where a person in an existing image or video is replaced with someone else's likeness. They are created using artificial intelligence and can be difficult to distinguish from real media. This project provides a solution to detect such deepfake videos effectively.

Features ✨

  • High Accuracy: Utilizes advanced neural networks to achieve high accuracy in detecting deepfakes.
  • Pre-trained Models: Leverages pre-trained CNN models for efficient feature extraction.
  • Temporal Modeling: Uses LSTM networks to analyze temporal aspects of videos.
  • User-friendly Interface: Easy-to-use interface for uploading and analyzing videos.

User Interface Screenshot

Installation 🛠️

To install and run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/reviverkid/DeepFakeDetectionSystem.git
cd deepfake-detection

  2. Create a virtual environment:

    python3 -m venv venv
source venv/bin/activate

  3. Install the required dependencies:

    pip install -r requirements.txt

  4. Download the pre-trained models:

  5. Navigate to the frontend directory and install dependencies:

    cd frontend
npm install

  6. Run the frontend:

    npm start

  7. Run the backend:

    python app.py

Usage 🚀

To use the Deepfake Detection System, follow these steps:

  1. Upload a video: Use the provided interface to upload the video you want to analyze.
  2. Analyze the video: Click on the "Analyze" button to start the detection process.
  3. View results: The system will display whether the video is a deepfake or not, along with a confidence score.

Model Architecture 🧠

The Deepfake Detection System uses a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Here’s an overview:

  • CNNs: Used for extracting spatial features from individual frames.
  • LSTM: Used for capturing temporal dependencies across frames.

Dataset 📚

The model is trained on a diverse dataset of real and fake videos. Some of the datasets used include:

  • DFDC (Deepfake Detection Challenge) Dataset
  • FaceForensics++

Results 📊

The Deepfake Detection System achieves high accuracy in detecting deepfakes. Here are some of the key metrics:

  • Accuracy: 78%
  • Precision: 77%
  • Recall: 96%

Technical Details 🛠️

The Deepfake Detection System leverages the following technologies and frameworks:

  • PyTorch: For building and training the deep learning models.
  • OpenCV: For video processing and frame extraction.
  • React: For creating the web-based frontend interface.
  • Firebase: For database management and user authentication.
  • Flask: For the backend API to interact with the model.

Key Components:

  • Pre-processing: Video frames are extracted and resized to a uniform dimension.
  • Feature Extraction: CNNs are used to extract meaningful features from each frame.
  • Temporal Analysis: LSTM networks process the sequence of frames to detect temporal inconsistencies indicative of deepfakes.
  • Post-processing: The results are aggregated and presented to the user with a confidence score.

Future Work 🔮

There are several avenues for improving and expanding the Deepfake Detection System:

  • Expand Dataset: Incorporate more diverse datasets to improve model robustness.
  • Enhance Model: Experiment with different architectures and hyperparameters to boost performance.
  • Real-time Detection: Optimize the system for real-time deepfake detection.
  • User Feedback: Integrate a feedback loop for users to report incorrect detections, helping to improve the model over time.
  • Mobile App: Develop a mobile application to make deepfake detection accessible on-the-go.

Acknowledgements 🙏

We would like to thank the following resources and communities for their invaluable support:

  • DFDC (Deepfake Detection Challenge) Dataset: For providing a comprehensive dataset for training and evaluation.
  • FaceForensics++: For offering additional datasets and benchmarks.
  • PyTorch Community: For their extensive documentation and support.

Contact 📬

For any questions or suggestions, feel free to contact me:

Thank you for checking out the Deepfake Detection System project! We hope you find it useful and look forward to your feedback. 😊