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arXiv · 2505.14862

Replay Attacks Against Audio Deepfake Detection

Nicolas Müller·Piotr Kawa·Wei-Herng Choong·Adriana Stan·Aditya Tirumala Bukkapatnam·Karla Pizzi·Alexander Wagner·Philip Sperl
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Abstract

We show how replay attacks undermine audio deepfake detection: By playing and re-recording deepfake audio through various speakers and microphones, we make spoofed samples appear authentic to the detection model. To study this phenomenon in more detail, we introduce ReplayDF, a dataset of recordings derived from M-AILABS and MLAAD, featuring 109 speaker-microphone combinations across six languages and four TTS models. It includes diverse acoustic conditions, some highly challenging for detection. Our analysis of six open-source detection models across five datasets reveals significant vulnerability, with the top-performing W2V2-AASIST model's Equal Error Rate (EER) surging from 4.7% to 18.2%. Even with adaptive Room Impulse Response (RIR) retraining, performance remains compromised with an 11.0% EER. We release ReplayDF for non-commercial research use.

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Nicolas Müller, Piotr Kawa, Wei-Herng Choong, Adriana Stan, Aditya Tirumala Bukkapatnam, Karla Pizzi, Alexander Wagner, Philip Sperl. 2025-05-20. Replay Attacks Against Audio Deepfake Detection. https://arxiv.org/abs/2505.14862

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