viktorahnstrom/xade-deepfake-detector

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

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

@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}}
}