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Deepfake Detection

Overview

This project aims to classify videos as "REAL" or "FAKE" using a deep learning model. The model architecture leverages the InceptionV3 model for feature extraction, followed by LSTM and GRU layers for sequence processing.

Deepfake Detection Web APP

Getting Started

Clone the Repository:

git clone https://github.com/your-repo-link.git
cd your-repo-directory

Install Dependencies:

pip install -r requirements.txt

Run the Application:

python app.py

Dataset

The dataset used for training and evaluation consists of:

  • 77 Fake Videos
  • 76 Real Videos

Each video has 10 frames extracted based on motion detection.

This newly motion detected dataset is released on kaggel as Deepfake Detection Challenge Dataset - Face Images.

Architecture

The model architecture consists of the following components:

  1. Feature Extraction:

    • InceptionV3 pre-trained on ImageNet is used for feature extraction.
    • GlobalAveragePooling2D is applied to pool features from each frame.

  2. Sequential Processing:

    • TimeDistributed applies the feature extractor to each frame independently.
    • LSTM and GRU layers are used to capture temporal dependencies.
    • Dropout is applied for regularization to prevent overfitting.
    • Dense layers are used for the final classification.

Training

The model was trained on Kaggle Notebooks with the following specifications:

Demo

You can try out the model live on Hugging Face Spaces: Demo

Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and create a pull request with your changes. Ensure your code adheres to the existing style and includes appropriate tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.