collaborators

5 papers

eess.AS2026

Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin

Octavian Pascu, Dan Oneata, Horia Cucu +1

Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers. The latter need reliable tools to verify the authenticity of their sou…

cs.SD2026

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…

cs.SD2026

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…

cs.SD2026

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…

cs.SD2026

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…