A state-of-the-art deepfake detection system built with PyTorch and EfficientNet-B0, featuring a user-friendly web interface for real-time image and video analysis.

• 👨‍💻 T RAHUL SINGH
• 🧑‍💻 Mallikarjun Macherla
• 🧑‍💻 Sainath

• Deep Learning Model: EfficientNet-B0 architecture fine-tuned for deepfake detection
• Multi-format Support: Analyze both images (.jpg, .jpeg, .png) and videos (.mp4, .mov)
• Web Interface: Interactive Gradio-based web application for easy testing
• Real-time Analysis: Process first frame of videos for quick deepfake detection
• Training Pipeline: Complete PyTorch Lightning training infrastructure
• Model Export: Support for PyTorch (.pt) and ONNX format exports

• Python 3.8 or higher
• CUDA-compatible GPU (optional, but recommended for training)

1. Clone the repository:

   git clone https://github.com/TRahulsingh/DeepfakeDetector.git cd DeepfakeDetector

2. Install dependencies:

   pip install -r requirements.txt

3. Download a pre-trained model (or train your own):

   • Place your model file as models/best_model-v3.pt

Launch the interactive web interface:

python web-app.py

The web app will open in your browser where you can:

• Drag and drop images or videos
• View real-time predictions with confidence scores
• See preview of analyzed content

Classify individual images:

python classify.py --image path/to/your/image.jpg

Process videos frame by frame:

python inference/video_inference.py --video path/to/your/video.mp4

This deepfake detection system supports various popular deepfake datasets. Below are the recommended datasets for training and evaluation:

• Description: One of the most comprehensive deepfake datasets with 4 manipulation methods
• Size: ~1,000 original videos, ~4,000 manipulated videos
• Manipulations: Deepfakes, Face2Face, FaceSwap, NeuralTextures
• Quality: Raw, c23 (light compression), c40 (heavy compression)
• Download: GitHub Repository
• Usage: Excellent for training robust models across different manipulation types

• Description: High-quality celebrity deepfake dataset
• Size: 590 real videos, 5,639 deepfake videos
• Quality: High-resolution with improved visual quality
• Download: Official Website
• Usage: Great for testing model performance on high-quality deepfakes

• Description: Facebook's large-scale deepfake detection dataset
• Size: ~100,000 videos (real and fake)
• Diversity: Multiple actors, ethnicities, and ages
• Download: Kaggle Competition
• Usage: Large-scale training and benchmarking

• Description: Google/Jigsaw deepfake dataset
• Size: ~3,000 deepfake videos
• Quality: High-quality with various compression levels
• Download: FaceForensics++ repository
• Usage: Additional training data for model robustness

• Description: Large collection of real and AI-generated face images
• Size: ~140,000 images
• Source: StyleGAN-generated faces vs real faces
• Download: Kaggle Dataset
• Usage: Perfect for image-based deepfake detection training

• Description: High-quality celebrity face dataset
• Size: 30,000 high-resolution images
• Quality: 1024×1024 resolution
• Download: GitHub Repository
• Usage: Real face examples for training

1. Download your chosen dataset from the links above
2. Extract to the data/ folder
3. Organize as shown in the training section below

Use our built-in tools to prepare datasets:

# Split video dataset into frames python tools/split_video_dataset.py --input_dir raw_videos --output_dir data # Split dataset into train/validation python tools/split_train_val.py --input_dir data --train_ratio 0.8 # General dataset splitting python tools/split_dataset.py --input_dir your_dataset --output_dir data

• For Beginners: Start with 140k Real and Fake Faces (image-based, easy to work with)
• For Research: Use FaceForensics++ (comprehensive, multiple manipulation types)
• For Production: Combine DFDC + Celeb-DF (large scale, diverse)
• For High-Quality Testing: Use Celeb-DF v2 (challenging, high-quality deepfakes)

• Ethical Use: These datasets are for research purposes only
• Legal Compliance: Ensure compliance with dataset licenses and terms of use
• Privacy: Respect privacy rights of individuals in the datasets
• Citation: Properly cite the original dataset papers when publishing research

Organize your training data in the data folder as follows:

data/ ├── train/ │ ├── real/ │ │ ├── image1.jpg │ │ └── image2.jpg │ └── fake/ │ ├── fake1.jpg │ └── fake2.jpg └── validation/ ├── real/ └── fake/ 

Update config.yaml with your dataset paths:

train_paths: - data/train val_paths: - data/validation lr: 0.0001 batch_size: 4 num_epochs: 10

python main_trainer.py

or

python model_trainer.py

The training will:

• Use PyTorch Lightning for efficient training
• Save best model based on validation loss
• Log metrics to TensorBoard
• Apply early stopping to prevent overfitting

View training progress with TensorBoard:

tensorboard --logdir lightning_logs

├── web-app.py # Main web application ├── main_trainer.py # Primary training script ├── classify.py # Image classification utility ├── realeval.py # Real-world evaluation script ├── config.yaml # Training configuration ├── requirements.txt # Python dependencies ├── README.md # Project documentation ├── LICENSE # MIT License ├── .gitignore # Git ignore rules ├── data/ # Dataset storage (not tracked by git) │ ├── train/ # Training data │ └── validation/ # Validation data ├── datasets/ │ └── hybrid_loader.py # Custom dataset loader ├── lightning_modules/ │ └── detector.py # PyTorch Lightning module ├── models/ │ └── best_model-v3.pt # Trained model weights ├── tools/ # Dataset preparation utilities │ ├── split_dataset.py │ ├── split_train_val.py │ └── split_video_dataset.py └── inference/ ├── export_onnx.py # ONNX export └── video_inference.py # Video processing 

• Backbone: EfficientNet-B0 (pre-trained on ImageNet)
• Classifier: Custom 2-class classifier with dropout (0.4)
• Input Size: 224x224 RGB images
• Output: Binary classification (Real/Fake) with confidence scores

The model achieves:

• High accuracy on diverse deepfake datasets
• Real-time inference capabilities
• Robust performance on compressed/low-quality media

Convert PyTorch model to ONNX format:

python inference/export_onnx.py

Process multiple files programmatically:

from web-app import predict_file results = [] for file_path in image_paths: prediction, confidence, preview = predict_file(file_path) results.append({ 'file': file_path, 'prediction': prediction, 'confidence': confidence })

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

• EfficientNet architecture by Google Research
• PyTorch Lightning for training infrastructure
• Gradio for web interface framework
• The research community for deepfake detection advances

This project is licensed under the MIT License.

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