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# One-Class Deepfake Detector
## Model Details
- **Architecture**: Hybrid CNN + ViT + FFT Frequency Features
- **Training Images**: 50,000 real images only
- **Final Radius**: 352.6608
- **Final Loss**: 124397.0862
- **Embedding Dim**: 512
## How It Works
This is a **one-class anomaly detector** using DeepSVDD (Deep Support Vector Data Description).
It learns a "hypersphere" around real images in embedding space. Images far from this
hypersphere are considered anomalous (fake).
## Usage
```python
import torch
from PIL import Image
import torchvision.transforms as transforms
# Load model
model_data = torch.load('deepfake_detector_model/deepsvdd_model.pth')
# Initialize your model and load weights
# model.load_state_dict(model_data['model_state_dict'])
# Preprocess image
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = Image.open('test.jpg').convert('RGB')
image_tensor = transform(image).unsqueeze(0)
# Get hypersphere distance
with torch.no_grad():
embedding = model(image_tensor)
distance = model.get_distance(embedding).item()
# Compare to threshold
threshold = model_data['radius'].item() * 1.5
is_fake = distance > threshold
print(f"Distance: {distance:.4f}, Threshold: {threshold:.4f}, Fake: {is_fake}")
```
## Files
- `deepsvdd_model.pth`: Complete model with center and radius
- `encoder.pth`: Encoder weights only
- `config.json`: Training configuration
- `training_history.json`: Training metrics
Generated: 2025-11-28 11:22:17
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