ReplayDF

ReplayDF is a dataset for evaluating the impact of replay attacks on audio deepfake detection systems. It features re-recorded bona-fide and synthetic speech derived from M-AILABS and MLAAD v5, using 109 unique speaker-microphone combinations across six languages and four TTS models in diverse acoustic environments.

This dataset reveals how such replays can significantly degrade the performance of state-of-the-art detectors. That is, audio deepfakes are detected much worse once they have been played over a loudspeaker and re-recorded via a microphone. It is provided for non-commercial research to support the development of robust and generalizable deepfake detection systems.

📄 Paper

Replay Attacks Against Audio Deepfake Detection (Interspeech 2025)

🔽 Download

sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/datasets/mueller91/ReplayDF

📌 Citation

@article{muller2025replaydf,
  title = {Replay Attacks Against Audio Deepfake Detection},
  author = {Nicolas Müller and Piotr Kawa and Wei-Herng Choong and Adriana Stan and Aditya Tirumala Bukkapatnam and Karla Pizzi and Alexander Wagner and Philip Sperl},
  journal={Interspeech 2025},
  year = {2025},
}

📁 Folder Structure

ReplayDF/
├── aux/
│   ├── <UID1>/                 # contains information such as setup, recorded sine sweep, RIR (derived from sine sweep)
│   ├── <UID2>/
│   └── ...
├── wav/
│   ├── <UID1>/
│   │   ├── spoof               # Re-recorded audio samples (spoofs)
│   │   ├── benign              # Re-recorded audio samples (benign)
│   │   └── meta.csv            # Metadata for this UID's recordings
│   ├── <UID2>/
│   │   ├── spoof               
│   │   ├── benign              
│   │   └── meta.csv            
│   └── ...
├── mos/
│   └── mos.png                 # MOS ratings plot
│   └── mos_scores              # individual mos scores

📄 License

Attribution-NonCommercial-ShareAlike 4.0 International: https://creativecommons.org/licenses/by-nc/4.0/

Resources

Find the original resources (i.e. non-airgapped audio files) here:

• MLAAD dataset v5, https://deepfake-total.com/mlaad.
• M-AILABS dataset, https://github.com/imdatceleste/m-ailabs-dataset.

Mic/Speaker Matrix

📊 Mean Opinion Scores (MOS)

The scoring criteria for rating the audio files are outlined in the table below:

Rating	Description	Speech Quality	Distortion (background noise, overdrive, etc.)
5	Excellent	Clear	Imperceptible
4	Good	Clear	Slightly perceptible, but not annoying
3	Fair	Understandable	Perceptible and slightly annoying
2	Poor	Understandable	Perceptible and annoying
1	Very Poor	Barely understandable	Very annoying and objectionable
e	Error	Inaudible	Heavy