README.md

metadata

title: Deepfake Image Classification
colorFrom: blue
colorTo: pink
sdk: keras
tags:
  - deepfake
  - image-classification
  - computer-vision
  - efficientnet
license: mit
app_file: Deepfake_Image_Classification.ipynb
model-index:
  - name: deepfake_image_classification_model
    results: []

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.
Balanced subset: 16,000 REAL + 16,000 FAKE images.

🚀 Usage

Install dependencies:

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