🧠 Deepfake Detection using Xception and TensorFlow

This repository contains code for building and training a deep learning model to detect deepfake images using the Xception architecture. The model is trained using TensorFlow on a dataset of real and fake images.

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📁 Dataset Structure

The dataset should be organized in the following structure:

• deepfake_dataset/
• ├── train/
• │ ├── Real/
• │ └── Fake/
• └── val/
• ├── Real/
• └── Fake/

Each folder (Real and Fake) should contain images representing that class.

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🚀 Model Architecture

We use the Xception architecture pre-trained on ImageNet, and add the following custom layers:

• GlobalAveragePooling2D
• Dense(128, activation='relu')
• Dropout(0.5)
• Dense(1, activation='sigmoid')

This model performs binary classification:

• 0 → Real
• 1 → Fake

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🧪 Requirements

Install the dependencies with: pip install -r requirements.txt

Required packages include:

• TensorFlow 2.x
• numpy
• opencv-python
• scikit-learn
• tqdm

🖥️ Hardware Used

• CPU: Intel® Xeon® Gold 6226R (64 cores)
• RAM: 384GB
• GPU: NVIDIA RTX A5000

📊 Sample Results

• Total Data:140000 (70000 Real +70000 Fake)
• Data Split:80:20

Metric Value

• Training Accuracy ~71%
• Validation Accuracy ~81%
• Final Val Loss ~0.47