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Redgerd/
XceptionNet-Keras

Name: XceptionNet-Keras
Rating: 2.5 (23 reviews)

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Deepfake Detection Model

This repository contains a deepfake detection model built using a combination of a pre-trained Xception network and an LSTM layer. The model is designed to classify videos as either "Real" or "Fake" by analyzing sequences of facial frames extracted from the video.

Model Architecture

The model architecture consists of the following components:

Input: Accepts a sequence of TIME_STEPS frames, each resized to 299x299 pixels.
Feature Extraction: A TimeDistributed Xception network processes each frame, extracting key features.
Temporal Learning: An LSTM layer with 256 units learns temporal dependencies between these extracted frame features.
Regularization: A Dropout layer (0.5 rate) prevents overfitting.
Output: A Dense layer with softmax activation predicts probabilities for "Real" and "Fake" classes.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was tested on unseen samples from the FaceForensics++ and CelebDFv2 datasets.

Metrics

Accuracy: Measures correct classifications.
F1 Score: Balances precision and recall.

Results

Metric	Value
Training Accuracy	98.44%
Validation Accuracy	97.05%
Test Accuracy	95.93%

Disclaimer: These results were obtained using the FaceForensics++ and CelebDFv2 datasets. Performance in real-world scenarios may vary.

How to Use

1. Setup

Clone the repository and install the required libraries:

pip install tensorflow opencv-python numpy mtcnn Pillow

2. Model Loading

The model weights are loaded from COMBINED_best_Phase1.keras. Ensure this file is accessible at the specified model_path.

python

model_path = ''
model = build_model() # Architecture defined in the `build_model` function
model.load_weights(model_path)

The build_model function defines the architecture as:

python

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Global parameters for model input shape (ensure these are defined before calling build_model)
# TIME_STEPS = 30
# HEIGHT = 299
# WIDTH = 299

def build_model(lstm_hidden_size=256, num_classes=2, dropout_rate=0.5):
    # Input shape: (batch_size, TIME_STEPS, HEIGHT, WIDTH, 3)
    inputs = layers.Input(shape=(TIME_STEPS, HEIGHT, WIDTH, 3))
    # TimeDistributed layer to apply the base model to each frame
    base_model = keras.applications.Xception(weights='imagenet', include_top=False, pooling='avg')
    # For inference, we don't need to set trainable, but if you plan to retrain, you can set accordingly
    # base_model.trainable = False
    # Apply TimeDistributed wrapper
    x = layers.TimeDistributed(base_model)(inputs)
    # x shape: (batch_size, TIME_STEPS, 2048)
    # LSTM layer
    x = layers.LSTM(lstm_hidden_size)(x)
    x = layers.Dropout(dropout_rate)(x)
    outputs = layers.Dense(num_classes, activation='softmax')(x)
    model = keras.Model(inputs, outputs)
    return model

3. Prediction

Once the video_array (preprocessed frames) is ready, you can make a prediction using the loaded model:

python

predictions = model.predict(video_array)
predicted_class = np.argmax(predictions, axis=1)[0]
probabilities = predictions[0]

class_names = ['Real', 'Fake']
print(f"Predicted Class: {class_names[predicted_class]}")
print(f"Class Probabilities: Real: {probabilities[0]:.4f}, Fake: {probabilities[1]:.4f}")

Redgerd/XceptionNet-Keras

Deepfake Detection Model

Model Architecture

Evaluation

Testing Data, Factors & Metrics

Testing Data

Metrics

Results

How to Use

1. Setup

2. Model Loading

3. Prediction

Redgerd/
XceptionNet-Keras