9 citations · 21 across the 6 of their papers we have counts for
6 papers
Physical Adversarial Examples for Multi-Camera Systems
Ana Răduţoiu, Jan-Philipp Schulze, Philip Sperl +1
Neural networks build the foundation of several intelligent systems, which, however, are known to be easily fooled by adversarial examples. Recent advances made these attacks possi…
An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples
Armin Ettenhofer, Jan-Philipp Schulze, Karla Pizzi
Audio adversarial examples are audio files that have been manipulated to fool an automatic speech recognition (ASR) system, while still sounding benign to a human listener. Most me…
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Jan-Philipp Schulze, Philip Sperl, Ana Răduţoiu +2
Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a…
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,…
Anomaly Detection by Recombining Gated Unsupervised Experts
J. -P. Schulze, P. Sperl, K. Böttinger
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some…
: Activation Anomaly Analysis
Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain informati…