29 citations · 92 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
Franziska Boenisch, Philip Sperl, Konstantin Böttinger
An important problem in deep learning is the privacy and security of neural networks (NNs). Both aspects have long been considered separately. To date, it is still poorly understoo…
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…
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…
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
J. -P. Schulze, P. Sperl, K. Böttinger
Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data,…