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Mueller
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# Audio files - uncompressed
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README.md
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---
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license: cc-by-nc-4.0
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task_categories:
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- audio-classification
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language:
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- en
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- fr
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- de
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- pl
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- es
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- it
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tags:
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- ReplayDF
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- Audio-Deepfake
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- Replay-Attack
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- Spoof
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- Replay
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pretty_name: ReplayDF
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size_categories:
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- 100K<n<1M
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---
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# ReplayDF
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**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.
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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.
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## 📄 Paper
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[Replay Attacks Against Audio Deepfake Detection (Interspeech 2025)](https://arxiv.org/pdf/2505.14862)
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## 🔽 Download
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```bash
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sudo apt-get install git-lfs
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git lfs install
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git clone https://huggingface.co/datasets/mueller91/ReplayDF
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```
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# 📌 Citation
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```
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@article{muller2025replaydf,
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title = {Replay Attacks Against Audio Deepfake Detection},
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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},
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journal={Interspeech 2025},
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year = {2025},
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}
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```
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# 📁 Folder Structure
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```
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ReplayDF/
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├── aux/
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│ ├── <UID1>/ # contains information such as setup, recorded sine sweep, RIR (derived from sine sweep)
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│ ├── <UID2>/
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│ └── ...
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├── wav/
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│ ├── <UID1>/
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│ │ ├── spoof # Re-recorded audio samples (spoofs)
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│ │ ├── benign # Re-recorded audio samples (benign)
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│ │ └── meta.csv # Metadata for this UID's recordings
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│ ├── <UID2>/
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│ │ ├── spoof
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│ │ ├── benign
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│ │ └── meta.csv
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│ └── ...
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├── mos/
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│ └── mos.png # MOS ratings plot
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│ └── mos_scores # individual mos scores
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```
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# 📄 License
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Attribution-NonCommercial-ShareAlike 4.0 International: https://creativecommons.org/licenses/by-nc/4.0/
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# Resources
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Find the original resources (i.e. non-airgapped audio files) here:
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- MLAAD dataset v5, https://deepfake-total.com/mlaad.
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- M-AILABS dataset, https://github.com/imdatceleste/m-ailabs-dataset.
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# Mic/Speaker Matrix
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# 📊 Mean Opinion Scores (MOS)
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The scoring criteria for rating the audio files are outlined in the table below:
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| Rating | Description | Speech Quality | Distortion (background noise, overdrive, etc.)|
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|-----------|---------------|---------------------------|-----------------------------------------------|
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| 5 | Excellent | Clear | Imperceptible |
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| 4 | Good | Clear | Slightly perceptible, but not annoying |
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| 3 | Fair | Understandable | Perceptible and slightly annoying |
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| 2 | Poor | Understandable | Perceptible and annoying |
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| 1 | Very Poor | Barely understandable | Very annoying and objectionable |
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| e | Error | Inaudible | Heavy |
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JBL Flip 4
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S2-010
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| 1 |
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Bose Mini II SE Soundlink
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VideoMic Pro (+20dB)
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ADDED
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Git LFS Details
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Dell Zylux Multimediasystem
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Logitech Z323
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Yeti USB
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Bose Mini II SE Soundlink
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UM10 USB
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Built-in speaker MacBook Air 2011
|
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Sennheiser Momentum 2 mic
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Soundbar SAMSUNG HW-B750D
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MacBook Pro 13 2020
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ThinkPad P15 Gen 1 built-in speaker
|
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ThinkPad P15 Gen 1 built-in microphone
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