28 citations · 28 across the 7 of their papers we have counts for
5 papers · 1 filter
Proteus: Automated Adversarial Robustness Testing for Audio Deepfake Detectors
Nicolas M. Müller, Aditya Tirumala Bukkapatnam, Zohaib Ahmed
We present Proteus, a framework developed at Resemble AI for automated robustness testing of our audio deepfake detection system. Given a detector, Proteus systematically searches…
Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception
Nicolas M. Müller, Wei Herng Choong
Audio deepfakes have improved rapidly recently, yet their effect on human trust in real speech remains unstudied. We present the largest listening study on audio deepfake perceptio…
Complex-valued neural networks for voice anti-spoofing
Nicolas M. Müller, Philip Sperl, Konstantin Böttinger
Current anti-spoofing and audio deepfake detection systems use either magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw audio processed through convoluti…
Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn?
Nicolas M. Müller, Franziska Dieckmann, Pavel Czempin +3
We present our analysis of a significant data artifact in the official 2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of silence duration in the training…
Towards Resistant Audio Adversarial Examples
Tom Dörr, Karla Markert, Nicolas M. Müller +1
Adversarial examples tremendously threaten the availability and integrity of machine learning-based systems. While the feasibility of such attacks has been observed first in the do…