Metadata-Version: 2.1
Name: Deepfake-detector
Version: 1.2
Summary: A Python library for detecting deepfake images and videos.
Home-page: https://github.com/saiadupa/Deepfake-detector
Author: Adupa Nithin Sai
Author-email: adupanithinsai@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3
Description-Content-Type: text/markdown
Requires-Dist: tensorflow==2.13.0
Requires-Dist: opencv-python==4.10.0.84
Requires-Dist: numpy==1.24.3
Requires-Dist: matplotlib==3.9.2
Requires-Dist: gdown

# Deepfake Detector

Deepfake Detector is a Python library designed for detecting deepfake content in images and videos. Leveraging advanced machine learning techniques, it provides an easy-to-use interface for real-time and batch processing of media files.

## Features

- Real-time Deepfake Detection: Analyze live video feeds to detect deepfake content.
- Batch Processing: Process multiple images and videos to classify them as real or fake.
- Customizable Thresholds: Adjust detection sensitivity to suit your specific needs.

## Installation

You can install the `deepfake_detector` package using pip:

```bash
pip install deepfake_detector
```

## Usage

### For Video Detection

Detect deepfake content in a video file. You can provide a video file path or use a webcam.

```python
from deepfake_detector import live_video_prediction, image_prediction

# Set a custom threshold
live_video_prediction(source='name.mp4',threshold=0.5)
```

### For Image Detection

Detect deepfake content in a single image.

```python
from deepfake_detector import live_video_prediction, image_prediction

# Image prediction example
image_prediction('name.png', threshold=0.6)
```

### Customizing Detection Threshold

You can set the threshold to control the sensitivity of the detection.

```python
# Example with a higher sensitivity threshold
live_video_prediction(source='name.mp4',threshold=0.5)
image_prediction('name.png', threshold=0.6)
```

## Paper Reference

For more detailed information about the techniques used in this library, please refer to the following research paper:

- **Title**: [Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision](https://www.mdpi.com/1424-8220/21/21/7367)
- **Journal**: MDPI Sensors
- **Abstract**: This paper explores advanced methods for detecting deepfake media using convolutional neural networks (CNNs). The study provides a comprehensive analysis of various techniques and their effectiveness in identifying manipulated content.

## Contributing

If you would like to contribute to the development of this library, please follow these steps:

1. Fork the repository on GitHub.
2. Create a new branch for your changes.
3. Commit your changes with descriptive messages.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.

## Contact

For any questions or feedback, please contact:

- **Author**: Adupa Nithin Sai
- **Email**: [adupanithinsai@gmail.com](mailto:adupanithinsai@gmail.com)
- **GitHub**: [https://github.com/saiadupa/Deepfake-detector](https://github.com/saiadupa/Deepfake-detector)

---

Thank you for using Deepfake Detector. We hope you find it useful for your deepfake detection needs!
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