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README.md

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-license: mit
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+Here is the updated markdown description for your model:
+
+---
+
+# 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.
+
+---