208 citations · 255 across the 4 of their papers we have counts for
4 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.…
[RE] CNN-generated images are surprisingly easy to spot...for now
Joel Frank, Thorsten Holz
This work evaluates the reproducibility of the paper "CNN-generated images are surprisingly easy to spot... for now" by Wang et al. published at CVPR 2020. The paper addresses the…
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…
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…