DF Arena 500M - Antispoofing Model
We are excited to release DF Arena 500M Universal Antispoofing model 🔥trained on traditional speech antispoofing datasets in addition to singing and environmental deepfake data. Check out the release on DF Arena leaderboard
Training Data
- ASVspoof 2019, 2024
- Codecfake
- LibriSeVoc
- DFADD
- CTRSVDD
- SpoofCeleb
- MLAAD
- EnvSDD
Usage
from transformers import pipeline
import librosa
#load model
pipe = pipeline("antispoofing", model="Speech-Arena-2025/DF_Arena_500M_V_1", trust_remote_code=True, device='cuda')
audio, sr = librosa.load("sample.wav", sr=16000)
result = pipe(audio)
print(result)
# Output:
{'label': 'spoof', 'logits': [[1.5515458583831787, -1.2254822254180908]], 'score': 0.9414217472076416, 'all_scores': {'spoof': 0.9414217472076416, 'bonafide': 0.05857823044061661}}
Evaluation
Evaluation
| Dataset | EER (%) | F1-score | Accuracy (%) |
|---|---|---|---|
| dfadd | 0.00 | 0.9993 | 99.97 |
| add_2023_round_2 | 12.30 | 0.9133 | 87.70 |
| codecfake | 6.36 | 0.8997 | 93.65 |
| asvspoof_2021_la | 4.23 | 0.8191 | 95.77 |
| in_the_wild | 1.76 | 0.9860 | 98.24 |
| asvspoof_2019 | 1.09 | 0.9494 | 98.91 |
| add_2022_track_1 | 23.98 | 0.6453 | 76.02 |
| fake_or_real | 2.30 | 0.9773 | 97.73 |
| asvspoof_2024 | 12.39 | 0.7423 | 87.61 |
| add_2022_track_3 | 2.77 | 0.9200 | 97.23 |
| add_2023_round_1 | 7.47 | 0.9465 | 92.53 |
| librisevoc | 0.12 | 0.9955 | 99.87 |
| asvspoof_2021_df | 3.30 | 0.6200 | 96.70 |
| sonar | 1.90 | 0.9837 | 98.13 |
| Average | 5.78 | 0.884 | 94.19 |
| Pooled | 10.88 | 0.78 | 89.11 |
License
We use a non-commercial license which can be found here
Contact
For questions or issues, please open an issue on the model repository or contact us at speech.arena.eval@gmail.com.
Stay tuned for upcoming versions of our models!
Citation
If you use this model in your work, it can be cited as :
@misc{kulkarni2026compactsslbackbonesmatter,
title={Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR},
author={Ajinkya Kulkarni and Sandipana Dowerah and Atharva Kulkarni and Tanel Alumäe and Mathew Magimai Doss},
year={2026},
eprint={2603.06164},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2603.06164},
}