DeepFake-Detector

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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:

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:

   git clone https://huggingface.co/MaanVad3r/DeepFake-Detector
   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.

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