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cs.LG2023★ 1 cited
On the Limitations of Model Stealing with Uncertainty Quantification Models
David Pape, Sina Däubener, Thorsten Eisenhofer +2
Model stealing aims at inferring a victim model's functionality at a fraction of the original training cost. While the goal is clear, in practice the model's architecture, weight d…
cs.LG2022★ 2 cited
How Sampling Impacts the Robustness of Stochastic Neural Networks
Sina Däubener, Asja Fischer
Stochastic neural networks (SNNs) are random functions whose predictions are gained by averaging over multiple realizations. Consequently, a gradient-based adversarial example is c…