---
license: mit
tags:
- deepfake-detection
- computer-vision
- efficientnet
---

# XADE Deepfake Detector

EfficientNet-B4 model trained for deepfake detection as part of the XADE (eXplainable Automated Deepfake Evaluation) thesis project.

## Model Details

- **Architecture:** EfficientNet-B4
- **Task:** Binary classification (real vs. fake faces)
- **Training Dataset:** 140k Real and Fake Faces
- **Test Accuracy:** 98.86%
- **AUC-ROC:** 99.94%

## Performance

| Metric | Value |
|--------|-------|
| Accuracy | 98.86% |
| Precision | 98.44% |
| Recall | 99.28% |
| F1-Score | 98.86% |

## Usage
```python
import torch
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="YOUR_USERNAME/xade-deepfake-detector",
    filename="best_model.pt"
)

# Load model
checkpoint = torch.load(model_path)
# ... (load into your model class)
```

## Training Details

- Samples: 100,000 training, 20,000 validation
- Epochs: 10 (early stopping)
- Optimizer: AdamW
- Learning rate: 0.001
- Batch size: 64

## Citation
```bibtex
@misc{xade2026,
  author = {Viktor Ahnström, Viktor Carlsson},
  title = {XADE: Cross-Platform Explainable Deepfake Detection Using Vision-Language Models},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/xade-deepfake-detector}}
}
```