---
license: cc-by-nc-4.0
task_categories:
  - audio-classification
language:
  - en
  - fr
  - de
  - pl
  - es
  - it
tags:
  - ReplayDF
  - Audio-Deepfake
  - Replay-Attack
  - Spoof
  - Replay
pretty_name: ReplayDF
size_categories:
  - 100K<n<1M
---

![logo](logo.png)  
![collage](collage.png)

# ReplayDF

**ReplayDF** is a dataset for evaluating the impact of **replay attacks** on audio deepfake detection systems. It features re-recorded synthetic speech derived from [M-AILABS](https://github.com/imdatceleste/m-ailabs-dataset) and [MLAAD v5](https://deepfake-total.com/mlaad), 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. 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)](https://arxiv.org/pdf/2505.14862)

## 🔽 Download
```bash
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
![MicSpeaker Matrix](mic_loudspeaker_matrix.png)

# 📊 Mean Opinion Scores (MOS)![mos](mos/mos.png)

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                                         |