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README.md

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+---
+tags:
+- image-classification
+- fake-detection
+- anomaly-detection
+- one-class-learning
+- deepfake-detection
+- computer-vision
+license: mit
+---
+
+# 🎯 Fake Image Detection Ensemble (9 Models)
+
+A powerful ensemble of 9 specialized models trained for detecting fake/AI-generated images using **single-class anomaly detection**. Trained only on real images to learn what "normal" looks like, then detects fakes as anomalies.
+
+## 📊 Performance
+
+| Metric | Score |
+|--------|-------|
+| **Accuracy** | 67.05% |
+| **Precision** | 87.97% |
+| **Recall** | 39.50% |
+| **F1 Score** | 54.52% |
+
+### Confusion Matrix
+- True Negatives: 946 (real correctly identified)
+- False Positives: 54 (real misclassified as fake)
+- False Negatives: 605 (fake misclassified as real)
+- True Positives: 395 (fake correctly identified)
+
+## 🏗️ Architecture
+
+The ensemble combines 9 specialized models using different detection strategies:
+
+### Deep Learning Models (3):
+1. **Enhanced Frequency VAE** - Multi-scale frequency analysis with phase information
+   - Uses both magnitude and phase of FFT
+   - Spectral consistency loss
+   - Detects frequency-domain artifacts
+
+2. **Edge Normalizing Flow** - Probability density estimation on edge features
+   - Multi-scale edge analysis
+   - Normalizing flow architecture
+   - Detects unnatural edge patterns
+
+3. **Semantic Deep SVDD** - ResNet50-based hypersphere anomaly detection
+   - Semantic feature extraction
+   - One-class deep learning
+   - Detects high-level semantic anomalies
+
+### Traditional ML Models (6):
+4. **Texture One-Class SVM** - Boundary-based detection
+   - Enhanced texture features
+   - RBF kernel
+   - Tight decision boundary (nu=0.03)
+
+5. **Isolation Forest** - Isolation-based anomaly detection
+   - 200 estimators
+   - Frequency + spatial features
+   - Fast inference
+
+6. **Local Outlier Factor** - Local density anomalies
+   - Multi-scale patch analysis
+   - Novelty detection mode
+   - 20 neighbors
+
+7. **Gaussian Mixture Model** - Distribution modeling
+   - 10 components
+   - Full covariance
+   - Color distribution analysis
+
+8. **Color Distribution Model** - Statistical color analysis
+   - RGB histograms
+   - Mahalanobis distance
+   - Color moment analysis
+
+9. **Statistical Model** - Edge and color statistics
+   - Sobel edge detection
+   - Multi-scale analysis
+   - Mahalanobis distance
+
+## 🎓 Training Details
+
+- **Training Data**: 30,000 real images from COCO dataset
+- **Training Approach**: Single-class anomaly detection (NO fake images used)
+- **Validation Split**: 20% (6,000 images)
+- **Test Set**: 1,000 real + 1,000 fake images (completely separate)
+- **Training Time**: ~5-6 hours on GPU
+- **Ensemble Method**: Weighted voting with adaptive threshold
+
+### Model Training Times (Extended):
+- Enhanced Frequency VAE: 45 minutes
+- Texture One-Class SVM: 45 minutes
+- Color Distribution Model: 30 minutes
+- Edge Normalizing Flow: 45 minutes
+- Semantic Deep SVDD: 45 minutes
+- Statistical Model: 30 minutes
+- Isolation Forest: 30 minutes
+- Local Outlier Factor: 35 minutes
+- Gaussian Mixture Model: 30 minutes
+
+## 🚀 Quick Start
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+import pickle
+import json
+from huggingface_hub import hf_hub_download
+
+# Configuration
+repo_id = "ash12321/fake-image-detection-ensemble"
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Download and load config
+config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+with open(config_path, 'r') as f:
+    config = json.load(f)
+
+# Load models (you need the model class definitions)
+# Example for one model:
+vae_path = hf_hub_download(repo_id=repo_id, filename="freq_vae.pth")
+# freq_vae = EnhancedFreqVAE()
+# freq_vae.load_state_dict(torch.load(vae_path, map_location=device))
+# freq_vae.to(device)
+
+# Load all other models similarly...
+
+# Predict on new image
+img = Image.open('test_image.jpg')
+img = img.resize((256, 256), Image.LANCZOS).convert('RGB')
+
+tfm = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])
+])
+img_tensor = tfm(img)
+
+# Get prediction from ensemble
+is_fake, score, individual_scores = ensemble.predict(img_tensor, device)
+print(f"Prediction: {'FAKE' if is_fake else 'REAL'}")
+print(f"Anomaly Score: {score:.4f}")
+print(f"Individual model scores: {individual_scores}")
+```
+
+## 📦 Model Files
+
+| File | Description | Size |
+|------|-------------|------|
+| `freq_vae.pth` | Enhanced Frequency VAE weights | ~100 MB |
+| `semantic_svdd.pth` | Semantic Deep SVDD weights | ~90 MB |
+| `edge_flow.pth` | Edge Normalizing Flow weights | ~5 MB |
+| `texture_ocsvm.pkl` | Texture One-Class SVM | ~200 MB |
+| `iforest.pkl` | Isolation Forest | ~150 MB |
+| `lof.pkl` | Local Outlier Factor | ~180 MB |
+| `gmm.pkl` | Gaussian Mixture Model | ~50 MB |
+| `color_model.pkl` | Color Distribution Model | ~10 MB |
+| `stat.pkl` | Statistical Model | ~5 MB |
+| `config.json` | Ensemble configuration | <1 MB |
+| `results_summary.json` | Training metrics | <1 MB |
+
+## 🔧 Requirements
+
+```
+torch>=2.0.0
+torchvision>=0.15.0
+numpy>=1.24.0
+pillow>=9.0.0
+scikit-learn>=1.3.0
+scipy>=1.10.0
+huggingface_hub>=0.19.0
+```
+
+## 🎯 Use Cases
+
+- **Deepfake Detection**: Identify AI-generated faces
+- **Image Forensics**: Detect manipulated images
+- **Content Moderation**: Filter synthetic content
+- **Research**: Study AI-generated image characteristics
+- **Quality Control**: Verify image authenticity
+
+## ⚠️ Limitations
+
+- Trained on COCO real images - performance may vary on other domains
+- Requires 256×256 input resolution
+- May struggle with heavily compressed or low-quality images
+- Performance depends on similarity between training and test distributions
+- Not designed for adversarial attacks
+
+## 📈 Model Improvements
+
+This version includes several accuracy enhancements:
+
+1. **Phase Information**: VAE uses both magnitude and phase of FFT
+2. **Enhanced Features**: More comprehensive texture and edge features
+3. **Adaptive Threshold**: Auto-calibrated at 95th percentile
+4. **Optimized Weights**: Balanced ensemble voting
+5. **Extended Training**: Up to 45 minutes per model for better convergence
+
+## 📝 Citation
+
+```bibtex
+@misc{fake-detection-ensemble-2024,
+  author = {ash12321},
+  title = {Fake Image Detection Ensemble - 9 Model System},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/ash12321/fake-image-detection-ensemble}}
+}
+```
+
+## 📄 License
+
+MIT License - Free for research and commercial use
+
+## 🙏 Acknowledgments
+
+- COCO Dataset for training data
+- PyTorch and scikit-learn communities
+- Hugging Face for model hosting
+
+## 📞 Contact
+
+Questions? Issues? Open an issue or discussion on this repository!
+
+---
+
+**Note**: This model was trained using single-class learning, making it robust to new types of fake images not seen during training. The ensemble approach combines multiple detection strategies for maximum accuracy and reliability.