Upload AI Image Detector model

README.md

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+---
+license: mit
+tags:
+- computer-vision
+- anomaly-detection
+- deep-svdd
+- ai-generated-images
+- image-classification
+- pytorch-lightning
+datasets:
+- cifar10
+library_name: pytorch-lightning
+pipeline_tag: image-classification
+---
+
+# 🔍 AI Image Detector - Deep SVDD
+
+<div align="center">
+
+**One-Class Deep Learning Model for Detecting AI-Generated Images**
+
+[![Model](https://img.shields.io/badge/Model-Deep%20SVDD-blue)](https://huggingface.co/ash12321/ai-image-detector-deepsvdd)
+[![Framework](https://img.shields.io/badge/Framework-PyTorch%20Lightning-red)](https://lightning.ai/)
+[![Dataset](https://img.shields.io/badge/Dataset-CIFAR--10-green)](https://www.cs.toronto.edu/~kriz/cifar.html)
+
+</div>
+
+## 📖 Model Description
+
+This model detects AI-generated images using **Deep Support Vector Data Description (SVDD)**, a one-class learning approach. It was trained exclusively on real images to learn what "real" looks like, allowing it to identify synthetic/AI-generated images as anomalies.
+
+### Key Features
+
+- ✅ **Enhanced Deep SVDD Architecture** with channel attention mechanisms
+- ✅ **Trained on 35,000 real images** from CIFAR-10 dataset
+- ✅ **L4 GPU Optimized** with mixed precision training (16-bit)
+- ✅ **Advanced Augmentation**: Mixup, multi-scale, contrastive learning
+- ✅ **Robust Evaluation**: 70/15/15 train/val/test split with unseen test data
+
+## 🎯 Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **Test Loss** | 0.7637 |
+| **Mean Distance** | 0.7637 |
+| **Std Distance** | 0.0024 |
+| **95th Percentile** | 0.7700 |
+| **Radius Threshold** | 0.7747 |
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install torch torchvision pytorch-lightning huggingface-hub pillow
+```
+
+### Basic Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import torchvision.transforms as transforms
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="ash12321/ai-image-detector-deepsvdd",
+    filename="model.ckpt"
+)
+
+# Load model (you'll need the model class definition)
+from model import AdvancedDeepSVDD
+
+model = AdvancedDeepSVDD.load_from_checkpoint(model_path)
+model.eval()
+
+# Prepare image
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.4914, 0.4822, 0.4465],
+        std=[0.2470, 0.2435, 0.2616]
+    )
+])
+
+image = Image.open('test_image.jpg').convert('RGB')
+image_tensor = transform(image).unsqueeze(0)
+
+# Predict
+is_fake, scores, distances = model.predict_anomaly(image_tensor)
+
+print(f"AI-Generated: {is_fake[0].item()}")
+print(f"Confidence: {scores[0].item()*100:.1f}%")
+print(f"Anomaly Score: {scores[0].item():.4f}")
+```
+
+### Using with Gradio
+
+```python
+import gradio as gr
+
+def predict(image):
+    img_tensor = transform(image).unsqueeze(0)
+    is_fake, scores, _ = model.predict_anomaly(img_tensor)
+    
+    result = "🚨 AI-Generated" if is_fake[0] else "✅ Real Image"
+    confidence = f"{scores[0].item()*100:.1f}%"
+    
+    return f"**{result}** (Confidence: {confidence})"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Markdown(),
+    title="AI Image Detector"
+)
+
+demo.launch()
+```
+
+## 🏗️ Architecture Details
+
+### Enhanced Deep SVDD Encoder
+
+```
+Input (3x32x32) 
+→ Stem Conv (64 channels)
+→ Layer1 (64→128) + Channel Attention
+→ Layer2 (128→256) + Channel Attention  
+→ Layer3 (256→512) + Channel Attention
+→ Dual Pooling (Avg + Max)
+→ Projection Head (1024→512→128)
+→ Output (128-dim latent space)
+```
+
+### Training Optimizations
+
+- **Optimizer**: AdamW (lr=1e-3, weight_decay=1e-3)
+- **Scheduler**: OneCycleLR with cosine annealing
+- **Batch Size**: 128 (L4 GPU optimized)
+- **Augmentation**: Mixup (α=0.2), multi-scale, extensive transforms
+- **Loss**: SVDD objective + contrastive diversity + L2 regularization
+
+## 📊 Training Configuration
+
+```python
+Model Parameters: 5.3M trainable
+Epochs: 30
+Training Samples: 35,000 (70%)
+Validation Samples: 7,500 (15%)
+Test Samples: 7,500 (15%)
+Precision: 16-bit mixed precision
+GPU: NVIDIA L4 with Tensor Cores
+```
+
+## 🎨 Data Augmentation Pipeline
+
+**Training Augmentations:**
+- Multi-scale resizing (32, 64, 96 pixels)
+- Random resized crop (scale: 0.5-1.0)
+- Random horizontal/vertical flips
+- Random rotation (±20°)
+- Color jitter (brightness, contrast, saturation, hue)
+- Gaussian blur
+- Random erasing
+- Mixup augmentation
+
+**Validation/Test:**
+- Simple resize to 32x32
+- Normalize with CIFAR-10 statistics
+
+## 💡 Use Cases
+
+- **Content Moderation**: Identify AI-generated images in uploads
+- **Digital Forensics**: Verify authenticity of images
+- **Research**: Study differences between real and synthetic images
+- **Education**: Demonstrate one-class learning techniques
+
+## ⚠️ Limitations
+
+- **Training Domain**: Optimized for natural images similar to CIFAR-10
+- **Image Size**: Trained on 32x32 images (resize larger images)
+- **Generalization**: May require fine-tuning for specific domains
+- **False Positives**: Unusual real images may be flagged as AI-generated
+- **Not Foolproof**: Sophisticated AI images may evade detection
+
+## 📚 Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{ai-image-detector-deepsvdd-2024,
+  author = {ash12321},
+  title = {AI Image Detector using Deep SVDD},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/ash12321/ai-image-detector-deepsvdd}},
+}
+```
+
+## 📄 License
+
+This model is released under the MIT License.
+
+## 🤝 Contributing
+
+Contributions, issues, and feature requests are welcome!
+
+## 👤 Author
+
+**ash12321**
+- Hugging Face: [@ash12321](https://huggingface.co/ash12321)
+
+## 🙏 Acknowledgments
+
+- CIFAR-10 dataset creators
+- PyTorch Lightning team
+- Deep SVDD paper authors
+- Hugging Face for hosting infrastructure
+
+---
+
+<div align="center">
+
+**[Try it on Hugging Face Spaces](https://huggingface.co/spaces/ash12321/ai-image-detector-demo)** 
+
+</div>

inference.py

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+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import torchvision.transforms as transforms
+
+# You'll need to include your model definition
+# Copy the AdvancedDeepSVDD class and related code here
+# or import from your training script
+
+def load_model(repo_id="ash12321/ai-image-detector-deepsvdd"):
+    """Download and load model from HuggingFace"""
+    
+    model_path = hf_hub_download(
+        repo_id=repo_id,
+        filename="model.ckpt"
+    )
+    
+    # Load model (requires model definition)
+    from model import AdvancedDeepSVDD
+    model = AdvancedDeepSVDD.load_from_checkpoint(model_path)
+    model.eval()
+    
+    return model
+
+def predict_image(image_path, model):
+    """Predict if image is AI-generated"""
+    
+    transform = transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor(),
+        transforms.Normalize(
+            mean=[0.4914, 0.4822, 0.4465],
+            std=[0.2470, 0.2435, 0.2616]
+        )
+    ])
+    
+    image = Image.open(image_path).convert('RGB')
+    image_tensor = transform(image).unsqueeze(0)
+    
+    with torch.no_grad():
+        is_fake, scores, distances = model.predict_anomaly(image_tensor)
+    
+    return {
+        'is_ai_generated': bool(is_fake[0].item()),
+        'confidence': float(scores[0].item()),
+        'anomaly_score': float(scores[0].item()),
+        'distance': float(distances[0].item())
+    }
+
+# Example usage
+if __name__ == "__main__":
+    model = load_model()
+    result = predict_image("test_image.jpg", model)
+    print(f"AI-Generated: {result['is_ai_generated']}")
+    print(f"Confidence: {result['confidence']*100:.1f}%")

model.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:613c522b1c13bb5621bf26b1e48e2b7c1f0fa8a4af5e013d8546b0cecaf2070f
+size 64014947

requirements.txt

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+torch>=2.0.0
+torchvision>=0.15.0
+pytorch-lightning>=2.0.0
+numpy>=1.24.0
+Pillow>=9.5.0
+huggingface-hub>=0.16.0