28 citations · 28 across the 3 of their papers we have counts for
7 papers
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…
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…
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…