
# DeepTracer

**DeepTracer** is a powerful Python tool designed to detect and analyze deepfake images and videos. Utilizing state-of-the-art deep learning techniques, it provides reliable predictions to identify manipulated media.

## Installation

You can install DeepTracer using pip:

```bash
pip install deeptracer
```

## Usage

Here’s how you can use the `deeptracer` package to detect deepfakes in images and videos.

### Import the DeepFakeDetector

To begin using DeepTracer, simply import the `DeepFakeDetector` class:

```python
from deeptracer import DeepFakeDetector
```

### Image Prediction

You can detect whether an image is a deepfake using the `predict_image` method. Here's an example:

```python
detector = DeepFakeDetector()
image_result = detector.predict_image('path\image.jpg')
print(image_result)
```

### Video Prediction

Similarly, you can analyze videos for deepfake detection using the `predict_video` method:

```python
detector = DeepFakeDetector()
video_result = detector.predict_video('path\video.mp4')
print(video_result)
```

### Expected Output

The output of both `predict_image` and `predict_video` methods will be in a format that indicates whether the media is likely to be real or fake, along with a confidence score. For example:

```python
{
  "label": "fake",  # or "real"
  "confidence": 0.95  # Confidence score in percentage
}
```

## Requirements

The following dependencies are required and automatically installed when using the package:

- `torch`
- `facenet-pytorch`
- `Pillow`
- `opencv-python`
- `numpy`
- `gdown`

These libraries are critical for the deep learning models and image/video processing.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Author

Developed by Vishwa. For any inquiries or suggestions, feel free to reach out at jvishu06@gmail.com.
