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 📌 Citation 📁 Folder Structure 📄 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 ra…
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