5 citations · 6 across the 3 of their papers we have counts for
4 papers
DETECT-3B-Omni is Agnostic of Content and Demographics
Nicolas M. Müller, Aditya Tirumala Bukkapatnam, Dominik Schnieders +1
A trustworthy and GDPR-compliant deepfake audio detector must base its decisions on acoustic artifacts, not on what is being said or who is speaking. We present a large-scale study…
MLAAD: The Multi-Language Audio Anti-Spoofing Dataset
Nicolas M. Müller, Piotr Kawa, Wei Herng Choong +6
This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 10: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It featu…
DeePen: Penetration Testing for Audio Deepfake Detection
Nicolas Müller, Piotr Kawa, Adriana Stan +5
Deepfakes - manipulated or forged audio and video media - pose significant security risks to individuals, organizations, and society at large. To address these challenges, machine…
Replay Attacks Against Audio Deepfake Detection
Nicolas Müller, Piotr Kawa, Wei-Herng Choong +5
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 a…