7 citations · 7 across the 3 of their papers we have counts for
4 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…
The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
Nicolas M. Müller, Nicolas M. Müller, Pascal Debus
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedic…
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…
Does Audio Deepfake Detection Generalize?
Nicolas M. Müller, Pavel Czempin, Franziska Dieckmann +2
Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techni…