66 citations · 109 across the 12 of their papers we have counts for
16 papers
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…
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…
Protecting Publicly Available Data With Machine Learning Shortcuts
Nicolas M. Müller, Maximilian Burgert, Pascal Debus +3
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalizatio…
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…
Localized Shortcut Removal
Nicolas M. Müller, Jochen Jacobs, Jennifer Williams +1
Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does n…
Attacker Attribution of Audio Deepfakes
Nicolas M. Müller, Franziska Dieckmann, Jennifer Williams
Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks…
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…
Human Perception of Audio Deepfakes
Nicolas M. Müller, Karla Pizzi, Jennifer Williams
The recent emergence of deepfakes has brought manipulated and generated content to the forefront of machine learning research. Automatic detection of deepfakes has seen many new ma…
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…
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks
Nicolas M. Müller, Simon Roschmann, Konstantin Böttinger
Data poisoning is one of the most relevant security threats against machine learning and data-driven technologies. Since many applications rely on untrusted training data, an attac…
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model
Edresson Casanova, Christopher Shulby, Eren Gölge +6
In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training. We propose a speaker-c…
Deep Reinforcement Learning for Backup Strategies against Adversaries
Pascal Debus, Nicolas Müller, Konstantin Böttinger
Many defensive measures in cyber security are still dominated by heuristics, catalogs of standard procedures, and best practices. Considering the case of data backup strategies, we…
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…
Data Poisoning Attacks on Regression Learning and Corresponding Defenses
Nicolas Michael Müller, Daniel Kowatsch, Konstantin Böttinger
Adversarial data poisoning is an effective attack against machine learning and threatens model integrity by introducing poisoned data into the training dataset. So far, it has been…
Identifying Mislabeled Instances in Classification Datasets
Nicolas Michael Müller, Karla Markert
A key requirement for supervised machine learning is labeled training data, which is created by annotating unlabeled data with the appropriate class. Because this process can in ma…