1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
Understanding the strengths and weaknesses of SSL models for audio deepfake model attribution
Gabriel Pîrlogeanu, Adriana Stan, Horia Cucu
Audio deepfake model attribution aims to mitigate the misuse of synthetic speech by identifying the source model responsible for generating a given audio sample, enabling accountab…
ADNAC: Audio Denoiser using Neural Audio Codec
Daniel Jimon, Mircea Vaida, Adriana Stan
Audio denoising is critical in signal processing, enhancing intelligibility and fidelity for applications like restoring musical recordings. This paper presents a proof-of-concept…
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…