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# CNN-Based Deepfake Detection Model
This repository contains a Convolutional Neural Network (CNN)-based model fine-tuned for deepfake detection. The model has been trained to classify images as either "real" or "fake" (deepfake) using a custom dataset of processed images.
## Model Overview
This model is a custom CNN architecture built specifically for deepfake detection. It has been designed to efficiently distinguish between real and fake images through a series of convolutional and pooling layers, followed by fully connected layers for classification.
### Key Features:
- **Model Architecture:** Convolutional Neural Network (CNN)
- **Input Size:** 128x128 pixels
- **Number of Classes:** 2 (Real, Fake)
- **Activation Function:** ReLU in hidden layers, Sigmoid for binary classification
- **Regularization:** L2 regularization and Dropout layers to prevent overfitting
- **Optimizer:** Adam with a learning rate of 0.0001
- **Training Epochs:** 100 epochs (with early stopping based on validation loss)
## Training Details
The model was trained on a custom dataset of real and deepfake images, using data augmentation techniques to improve generalization. The training process involved the following components:
- **Data Augmentation:** Random rotations, shifts, flips, and brightness adjustments.
- **Loss Function:** Binary Cross-Entropy Loss
- **Optimizer:** Adam with a learning rate of 0.0001
- **Callbacks:** Early stopping, learning rate scheduler, and model checkpointing were used to optimize training.
## Model Performance
The model was evaluated on a held-out test set. Below is the key performance metric:
- **Test Accuracy:** 71%
This accuracy reflects the model's ability to correctly identify real and deepfake images.
## Usage
You can use this model for inference by loading the model and running predictions on new images. Below is an example using TensorFlow/Keras:
```python
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
# Load the trained model
model = load_model('cnn_model.h5')
# Load and preprocess the image
img_path = 'path_to_your_image.jpg'
img = image.load_img(img_path, target_size=(128, 128))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Make a prediction
prediction = model.predict(img_array)
print('Real' if prediction[0][0] < 0.5 else 'Fake')
```
## How to Use
1. **Clone the repository**:
```bash
git clone https://github.com/MaanVader/DeepFake-Detection-model.git
cd DeepFake-Detection-model.git
```
2. **Run Inference**:
Use the provided script or the sample code above to run inference on your images.
## License
This project is licensed under the MIT License. Feel free to use and modify the model as needed.
## Acknowledgments
Thanks to the various open-source projects and contributors whose work has made this project possible.
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