# Residual Convolutional Autoencoder for Deepfake Detection

Multi-dataset trained model with **19.18x separation** between real and fake images.

## Model Performance

- **Training Time**: 21.4 minutes on H200 GPU
- **Best Validation Loss**: 0.007970 (Epoch 29)
- **Anomaly Separation**: 19.18x (fake images have 19x higher reconstruction error)
- **Datasets**: CIFAR-10, CIFAR-100, STL-10 (205,000 training images)

## Quick Start
```python
from huggingface_hub import hf_hub_download
import torch
from model import ResidualConvAutoencoder
from torchvision import transforms
from PIL import Image
import json

# Download model and thresholds
checkpoint_path = hf_hub_download(
    repo_id="ash12321/deepfake-autoencoder-cifar10-v2", 
    filename="model_universal_best.ckpt"
)
threshold_path = hf_hub_download(
    repo_id="ash12321/deepfake-autoencoder-cifar10-v2", 
    filename="thresholds_calibrated.json"
)

# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResidualConvAutoencoder(latent_dim=512, dropout=0.1).to(device)
checkpoint = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Load thresholds
with open(threshold_path) as f:
    thresholds = json.load(f)

# Prepare image
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

image = Image.open("your_image.jpg").convert('RGB')
image_tensor = transform(image).unsqueeze(0).to(device)

# Get reconstruction error
with torch.no_grad():
    error = model.reconstruction_error(image_tensor)
    error_value = error.item()
    print(f"Reconstruction error: {error_value:.6f}")

# Check against threshold (balanced mode)
balanced_threshold = thresholds['reconstruction_thresholds']['thresholds']['balanced']['value']
if error_value > balanced_threshold:
    print("⚠️  Potential deepfake detected!")
else:
    print("✅ Image appears authentic")
```

## Detection Thresholds

Three calibrated threshold levels:

| Mode | Threshold | False Positive Rate | Description |
|------|-----------|---------------------|-------------|
| **Strict** | 0.055737 | ~1% | Very low false positives |
| **Balanced** | 0.039442 | ~5% | Recommended for general use |
| **Sensitive** | ~0.039 | ~2.5% | More sensitive detection |

## Model Architecture

- **Encoder**: 5 downsampling blocks (128→64→32→16→8→4)
- **Latent Space**: 512 dimensions
- **Decoder**: 5 upsampling blocks (4→8→16→32→64→128)
- **Residual Blocks**: Skip connections with dropout (0.1)
- **Total Parameters**: ~40M

## Training Details

- **Epochs**: 30 (best at epoch 29)
- **Batch Size**: 1024
- **Optimizer**: AdamW (lr=1e-4, weight_decay=1e-5)
- **Scheduler**: Cosine Annealing with Warm Restarts
- **Data Augmentation**: Horizontal flip, color jitter
- **Mixed Precision**: AMP enabled

## Statistics

### Real Images
- Mean error: 0.018391
- Median error: 0.015647
- Std: 0.010279
- 95th percentile: 0.039442
- 99th percentile: 0.055737

### Fake Images (Synthetic)
- Mean error: 0.352695
- Median error: 0.347151

**Separation Ratio**: 19.18x 🎯

## Files

- `model_universal_best.ckpt` - Full checkpoint (418MB)
- `thresholds_calibrated.json` - Calibrated thresholds
- `model.py` - Model architecture
- `config.json` - Training configuration
- `README.md` - This file

## Citation
```bibtex
@misc{deepfake_autoencoder_2024,
  title={Residual Convolutional Autoencoder for Deepfake Detection},
  author={Your Name},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/ash12321/deepfake-autoencoder-cifar10-v2}
}
```

## License

MIT License