AuthentiVision 🔍

State-of-the-art Face Authentication Model for Detecting AI-Generated Images

Github | Data | Demo | Tech Blog

🎯 Real vs. AI-Generated Face Comparison

Real Face	AI-Generated Face

🌟 Features

• High accuracy in distinguishing real faces from AI-generated ones
• Multiple feature extraction techniques for robust detection
• Easy-to-use API for quick integration
• Lightweight and efficient inference
• Comprehensive documentation and examples

🚀 Quick Start

git clone https://github.com/TimeLabHub/AuthentiVision.git
cd AuthentiVision
pip install -r requirements.txt

from authentivision import AuthentiVision

# Initialize detector
detector = AuthentiVision()

# Make prediction
label, confidence = detector.predict("path_to_image.jpg")
print(f"Prediction: {label} (Confidence: {confidence:.2f})")

📚 Documentation

For detailed documentation, please visit our tech blog.

🎯 Use Cases(Coming soon)

• Identity verification systems
• Social media content moderation
• Digital forensics
• Security applications

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🌟 Acknowledgments

• Thanks to all contributors and researchers in the field
• Special thanks to the open-source community

📝 Citation

If you use AuthentiVision in your research or project, please cite our technical blog

@online{authentivision2024,
    title={AuthentiVision: Finding Yourself in the Real World},
    author={Haijian Wang and Zhangbei Ding and Yefan Niu and Xiaoming Zhang},
    year={2024},
    url={https://timelabhub.github.io/},
    note={Medium blog post}
}