This project focuses on identifying and mitigating the spread of deepfake content using advanced machine learning techniques. Deepfakes are synthetic media where a person in an existing image or video is replaced with someone else's likeness, often leading to misinformation and privacy concerns.
- Deepfake Identification: Utilizes state-of-the-art algorithms to detect manipulated media.
- Robust Model Training: Employs a diverse dataset to train models capable of distinguishing between authentic and altered content.
- User-Friendly Interface: Provides tools for users to input media and receive analysis results.
- Clone the Repository:
git clone https://github.com/anandharshit712/Deepfake_Detection.git cd Deepfake_Detection - Install Dependencies:
pip install -r requirements.txt
- Prepare the Dataset:
- Ensure the dataset is organized appropriately for training and testing.
- Train the Model:
python train_model.py
- Evaluate the Model:
python evaluate_model.py
- Detect Deepfakes:
python detect.py --input /path/to/media
The project utilizes a comprehensive dataset containing both genuine and manipulated media to train the detection model effectively. For more information on the dataset, please refer to the project's documentation.
The detection system is built using a Convolutional Neural Network (CNN) architecture, designed to capture subtle inconsistencies in media that indicate manipulation. The model is implemented using TensorFlow, a popular deep learning framework.
Special thanks to the contributors of the datasets and open-source projects that made this work possible.
Note: This README provides an overview of the Deepfake Detection project. For detailed information, please refer to the project's documentation and source code.