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license: apache-2.0 |
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# AuthentiVision π |
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<div align="center"> |
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<img src="assets/img_1.jpg" alt="Logo" width="300"/> |
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**State-of-the-art Face Authentication Model for Detecting AI-Generated Images** |
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[Github](https://github.com/TimeLabHub/AuthentiVision) | [Data](https://huggingface.co/datasets/haijian06/face-auth-dataset) | [Demo](https://huggingface.co/spaces/haijian06/TrueFace) | [Tech Blog](https://timelabhub.github.io/) |
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</div> |
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</div> |
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## π― Real vs. AI-Generated Face Comparison |
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<div align="center"> |
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<table> |
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<tr> |
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<td><b>Real Face</b></td> |
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<td><b>AI-Generated Face</b></td> |
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</tr> |
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<tr> |
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<td> |
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<img src="assets/real_face.jpg" alt="Real Face" width="200"/> |
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</td> |
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<td> |
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<img src="assets/ai_face.jpg" alt="AI-Generated Face" width="200"/> |
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</td> |
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</tr> |
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<tr> |
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<td> |
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<img src="assets/real_face_2.jpg" alt="Real Face" width="200"/> |
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</td> |
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<td> |
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<img src="assets/ai_face_2.jpg" alt="AI-Generated Face" width="200"/> |
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</td> |
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</tr> |
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</table> |
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</div> |
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## π Features |
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- High accuracy in distinguishing real faces from AI-generated ones |
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- Multiple feature extraction techniques for robust detection |
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- Easy-to-use API for quick integration |
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- Lightweight and efficient inference |
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- Comprehensive documentation and examples |
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## π Quick Start |
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```bash |
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git clone https://github.com/TimeLabHub/AuthentiVision.git |
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cd AuthentiVision |
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pip install -r requirements.txt |
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``` |
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```python |
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from authentivision import AuthentiVision |
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# Initialize detector |
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detector = AuthentiVision() |
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# Make prediction |
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label, confidence = detector.predict("path_to_image.jpg") |
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print(f"Prediction: {label} (Confidence: {confidence:.2f})") |
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``` |
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## π Documentation |
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For detailed documentation, please visit our [tech blog](https://timelabhub.github.io/). |
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## π― Use Cases(Coming soon) |
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- Identity verification systems |
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- Social media content moderation |
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- Digital forensics |
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- Security applications |
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## π License |
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This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. |
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## π Acknowledgments |
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- Thanks to all contributors and researchers in the field |
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- Special thanks to the open-source community |
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## π Citation |
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If you use AuthentiVision in your research or project, please cite our technical blog: |
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```bibtex |
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@online{authentivision2024, |
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title={AuthentiVision: Finding Yourself in the Real World}, |
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author={Haijian Wang and Zhangbei Ding and Yefan Niu and Xiaoming Zhang}, |
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year={2024}, |
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url={https://timelabhub.github.io/}, |
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note={Medium blog post} |
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} |
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