Replay Attacks Against Audio Deepfake Detection

Aditya Tirumala Bukkapatnam; Adriana Stan; Alexander Wagner; Karla Pizzi; Nicolas M\"uller; Philip Sperl; Piotr Kawa; Wei-Herng Choong

Replay Attacks Against Audio Deepfake Detection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2505.14862 v2 pith:DWVQ4YSM submitted 2025-05-20 cs.SD cs.AIeess.AS

Replay Attacks Against Audio Deepfake Detection

Nicolas M\"uller , Piotr Kawa , Wei-Herng Choong , Adriana Stan , Aditya Tirumala Bukkapatnam , Karla Pizzi , Alexander Wagner , Philip Sperl This is my paper

classification cs.SD cs.AIeess.AS

keywords detectionaudiodeepfakeacrossattacksmodelmodelsreplay

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original 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.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Alethia: A Foundational Encoder for Voice Deepfakes
cs.SD 2026-04 unverdicted novelty 6.0

Alethia is a pretrained audio encoder using continuous embedding prediction and generative flow-matching reconstruction that outperforms existing speech foundation models on voice deepfake tasks with better robustness...