5 papers
Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks
Matías Pizarro, Raghavan Narasimhan, Jonas Killian +1
With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become highly relevant. We obs…
Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints
Matías Pizarro, Mike Laszkiewicz, Dorothea Kolossa +1
As speech generation technologies advance, so do risks of impersonation, misinformation, and spoofing. We present a lightweight, training-free approach for detecting synthetic spee…
Comparative Study on Noise-Augmented Training and its Effect on Adversarial Robustness in ASR Systems
Karla Pizzi, Matías Pizarro, Asja Fischer
In this study, we investigate whether noise-augmented training can concurrently improve adversarial robustness in automatic speech recognition (ASR) systems. We conduct a comparati…
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution
Matías Pizarro, Dorothea Kolossa, Asja Fischer
Adversarial attacks can mislead automatic speech recognition (ASR) systems into predicting an arbitrary target text, thus posing a clear security threat. To prevent such attacks, w…
Robustifying automatic speech recognition by extracting slowly varying features
Matías Pizarro, Dorothea Kolossa, Asja Fischer
In the past few years, it has been shown that deep learning systems are highly vulnerable under attacks with adversarial examples. Neural-network-based automatic speech recognition…