Search Results for author:
Found 6 papers, 5 papers with code
A Representative Study on Human Detection of Artificially Generated Media Across Countries
To further understand which factors influence people's ability to detect generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research.
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Deep generative modeling has the potential to cause significant harm to society.
[RE] CNN-generated images are surprisingly easy to spot...for now
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.
Dompteur: Taming Audio Adversarial Examples
In this paper we propose a different perspective: We accept the presence of adversarial examples against ASR systems, but we require them to be perceivable by human listeners.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Efficient Calculation of Adversarial Examples for Bayesian Neural Networks
In this paper we propose to efficiently attack Bayesian neural networks with adversarial examples calculated for a deterministic network with parameters given by the mean of the posterior distribution.
Leveraging Frequency Analysis for Deep Fake Image Recognition
Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
