| | --- |
| | 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}} |
| | } |
| | ``` |
