5 citations · 6 across the 3 of their papers we have counts for
6 papers
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…
APEX: Audio Prototype EXplanations for Classification Tasks
Piotr Kawa, Kornel Howil, Piotr Borycki +3
Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods apply vision-based attribution te…
As Good as It KAN Get: High-Fidelity Audio Representation
Patryk MarszaÅek, Maciej Rut, Piotr Kawa +2
Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces…
Are audio DeepFake detection models polyglots?
BartÅomiej Marek, Piotr Kawa, Piotr Syga
Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In thi…
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…