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README.md

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-license: mit
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+# Deepfake Image Classification
+
+This repository contains a TensorFlow/Keras model trained to classify **REAL vs FAKE (deepfake) images**.  
+The model is based on **EfficientNetB4** with data augmentation and fine-tuning.  
+
+## 📂 Files
+- `deepfake_image_classification_model.keras` – trained Keras model.  
+- `Deepfake_Image_Classification.ipynb` – full training notebook (Google Colab).  
+
+## 🧠 Model Details
+- Backbone: EfficientNetB4 (pretrained on ImageNet).  
+- Input size: `224 x 224 x 3` RGB images.  
+- Output: Binary classification (REAL = 0, FAKE = 1).  
+- Loss: `binary_crossentropy`.  
+- Optimizer: `Adam (lr=0.001)`.  
+
+## 📊 Dataset
+Trained on [Deepfake Faces dataset](https://www.kaggle.com/datasets/dagnelies/deepfake-faces).  
+Balanced subset: 16,000 REAL + 16,000 FAKE images.  
+
+## 🚀 Usage
+Install dependencies:
+```bash
+pip install tensorflow
+Load and use the model:
+
+python
+Copy code
+import tensorflow as tf
+
+# Load model
+model = tf.keras.models.load_model("deepfake_image_classification_model.keras")
+
+# Preprocess image
+img = tf.keras.utils.load_img("example.jpg", target_size=(224, 224))
+x = tf.keras.utils.img_to_array(img)
+x = tf.expand_dims(x, axis=0)  # add batch dimension
+
+# Predict
+pred = model.predict(x)[0][0]
+label = "FAKE" if pred > 0.5 else "REAL"
+print(f"Prediction: {label} (score: {pred:.4f})")
+📈 Performance
+Validation Accuracy: ~92%
+
+Test Accuracy: ~91%
+
+📓 Training
+For full details of the training pipeline (dataset loading, augmentation, callbacks, etc.), check the notebook:
+Deepfake_Image_Classification.ipynb