| # 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 |
|
|
