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… See the full description on the dataset page:
https://huggingface.co/datasets/mueller91/ReplayDF.
