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| title: DeepFake Detector API | |
| emoji: 🎭 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: docker | |
| app_port: 7860 | |
| # 🎭 DeepFake Detector API | |
| FastAPI backend for detecting AI-generated (deepfake) images using an ensemble of state-of-the-art deep learning models. | |
| ## 🤖 Models | |
| This API uses a fusion ensemble of 5 deep learning models: | |
| - **CNN Transfer** (EfficientNet-B0) - Transfer learning from ImageNet | |
| - **ViT Base** (Vision Transformer) - Attention-based architecture | |
| - **DeiT Distilled** (Data-efficient Image Transformer) - Distilled ViT variant | |
| - **Gradient Field CNN** - Custom architecture analyzing gradient patterns | |
| - **FFT CNN** - Frequency domain analysis using Fast Fourier Transform | |
| All models are combined using a **Logistic Regression stacking ensemble** for optimal accuracy. | |
| ## 🔗 API Endpoints | |
| | Endpoint | Method | Description | | |
| |----------|--------|-------------| | |
| | `/health` | GET | Health check - returns API status | | |
| | `/ready` | GET | Model readiness check - confirms models are loaded | | |
| | `/models` | GET | List all loaded models with metadata | | |
| | `/predict` | POST | Predict if an image is real or AI-generated | | |
| | `/docs` | GET | Interactive Swagger API documentation | | |
| | `/redoc` | GET | Alternative API documentation | | |
| ## 🚀 Usage Example | |
| ### Using cURL | |
| ```bash | |
| # Check if API is ready | |
| curl https://lukhsaankumar-deepfakedetectorbackend.hf.space/ready | |
| # Make a prediction | |
| curl -X POST "https://lukhsaankumar-deepfakedetectorbackend.hf.space/predict" \ | |
| -F "file=@image.jpg" \ | |
| -F "explain=true" | |
| ``` | |
| ### Using Python | |
| ```python | |
| import requests | |
| # Upload an image for prediction | |
| url = "https://lukhsaankumar-deepfakedetectorbackend.hf.space/predict" | |
| files = {"file": open("image.jpg", "rb")} | |
| data = {"explain": True} | |
| response = requests.post(url, files=files, data=data) | |
| result = response.json() | |
| print(f"Prediction: {result['prediction']}") | |
| print(f"Confidence: {result['confidence']:.2%}") | |
| print(f"Explanation: {result['explanation']}") | |
| ``` | |
| ## 🎯 Response Format | |
| ```json | |
| { | |
| "prediction": "fake", | |
| "confidence": 0.8734, | |
| "probabilities": { | |
| "real": 0.1266, | |
| "fake": 0.8734 | |
| }, | |
| "model_predictions": { | |
| "cnn_transfer": {"prediction": "fake", "confidence": 0.89}, | |
| "vit_base": {"prediction": "fake", "confidence": 0.92}, | |
| "deit": {"prediction": "fake", "confidence": 0.85}, | |
| "gradient_field": {"prediction": "real", "confidence": 0.55}, | |
| "fft_cnn": {"prediction": "fake", "confidence": 0.78} | |
| }, | |
| "fusion_confidence": 0.8734, | |
| "explanation": "AI-powered analysis of the prediction...", | |
| "processing_time_ms": 342 | |
| } | |
| ``` | |
| ## 🔧 Configuration | |
| ### Required Secrets | |
| Set these in your Space Settings → Repository secrets: | |
| | Secret | Description | Required | | |
| |--------|-------------|----------| | |
| | `GOOGLE_API_KEY` | Google Gemini API key for AI explanations | Yes | | |
| | `HF_TOKEN` | Hugging Face token (auto-set by Spaces) | No | | |
| ### Optional Environment Variables | |
| | Variable | Default | Description | | |
| |----------|---------|-------------| | |
| | `HF_FUSION_REPO_ID` | `DeepFakeDetector/fusion-logreg-final` | Hugging Face model repository | | |
| | `CORS_ORIGINS` | Multiple defaults | Comma-separated allowed CORS origins | | |
| | `GEMINI_MODEL` | `gemini-2.5-flash` | Gemini model for explanations | | |
| ## 🏗️ Architecture | |
| ``` | |
| ┌─────────────┐ | |
| │ Client │ | |
| └──────┬──────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────┐ | |
| │ FastAPI Backend │ | |
| │ ┌──────────────────────────┐ │ | |
| │ │ Model Registry │ │ | |
| │ │ ┌────────────────────┐ │ │ | |
| │ │ │ CNN Transfer │ │ │ | |
| │ │ │ ViT Base │ │ │ | |
| │ │ │ DeiT Distilled │ │ │ | |
| │ │ │ Gradient Field │ │ │ | |
| │ │ │ FFT CNN │ │ │ | |
| │ │ └────────────────────┘ │ │ | |
| │ │ ┌────────────────────┐ │ │ | |
| │ │ │ Fusion Ensemble │ │ │ | |
| │ │ │ (LogReg Stacking) │ │ │ | |
| │ │ └────────────────────┘ │ │ | |
| │ └──────────────────────────┘ │ | |
| │ ┌──────────────────────────┐ │ | |
| │ │ Gemini Explainer │ │ | |
| │ └──────────────────────────┘ │ | |
| └─────────────────────────────────┘ | |
| ``` | |
| ## 📊 Performance | |
| - **Accuracy**: ~87% on test set (OpenFake dataset) | |
| - **Inference Time**: ~200-500ms per image (with GPU) | |
| - **Model Size**: ~500MB total | |
| - **Supported Formats**: JPG, PNG, WEBP | |
| ## 🐛 Troubleshooting | |
| ### Models not loading? | |
| - Check the Logs tab for specific errors | |
| - Verify `HF_FUSION_REPO_ID` points to a valid repository | |
| - Ensure the repository is public or `HF_TOKEN` is set | |
| ### Explanations not working? | |
| - Verify `GOOGLE_API_KEY` is set in Space Settings | |
| - Check if you have Gemini API quota remaining | |
| - Review logs for API errors | |
| ### CORS errors? | |
| - Add your frontend domain to `CORS_ORIGINS` in Space Settings | |
| - Format: `https://yourdomain.com,https://www.yourdomain.com` | |
| ## 📚 Documentation | |
| - **Interactive Docs**: Visit `/docs` for Swagger UI | |
| - **ReDoc**: Visit `/redoc` for alternative documentation | |
| - **Source Code**: [GitHub Repository](https://github.com/lukhsaankumar/DeepFakeDetector) | |
| ## 📝 License | |
| This project is part of the MacAI Society research initiative. | |
| ## 🙏 Acknowledgments | |
| - Models trained on OpenFake, ImageNet, and custom datasets | |
| - Powered by PyTorch, Hugging Face, and FastAPI | |
| - AI explanations by Google Gemini | |
| --- | |
| **Built with ❤️ by MacAI Society** | |