45 citations · 61 across the 2 of their papers we have counts for
6 papers
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Joel Frank, Lea Schönherr
Deep generative modeling has the potential to cause significant harm to society. Recognizing this threat, a magnitude of research into detecting so-called "Deepfakes" has emerged.…
Dompteur: Taming Audio Adversarial Examples
Thorsten Eisenhofer, Lea Schönherr, Joel Frank +3
Adversarial examples seem to be inevitable. These specifically crafted inputs allow attackers to arbitrarily manipulate machine learning systems. Even worse, they often seem harmle…
Unacceptable, where is my privacy? Exploring Accidental Triggers of Smart Speakers
Lea Schönherr, Maximilian Golla, Thorsten Eisenhofer +3
Voice assistants like Amazon's Alexa, Google's Assistant, or Apple's Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households.…
Leveraging Frequency Analysis for Deep Fake Image Recognition
Joel Frank, Thorsten Eisenhofer, Lea Schönherr +3
Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements ha…
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems
Lea Schönherr, Thorsten Eisenhofer, Steffen Zeiler +2
Automatic speech recognition (ASR) systems can be fooled via targeted adversarial examples, which induce the ASR to produce arbitrary transcriptions in response to altered audio si…
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding
Lea Schönherr, Katharina Kohls, Steffen Zeiler +2
Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR),…