29 citations · 77 across the 22 of their papers we have counts for
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cs.CR2021★ 8 cited
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
Franziska Boenisch, Philip Sperl, Konstantin Böttinger
An important problem in deep learning is the privacy and security of neural networks (NNs). Both aspects have long been considered separately. To date, it is still poorly understoo…
cs.LG2021★ 7 cited
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,…