19 citations · 36 across the 14 of their papers we have counts for
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cs.CR2019★ 15 cited
Assessing differentially private deep learning with Membership Inference
Daniel Bernau, Philip-William Grassal, Jonas Robl +1
Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible…
cs.LG2019★ 19 cited
On the Robustness of the Backdoor-based Watermarking in Deep Neural Networks
Masoumeh Shafieinejad, Jiaqi Wang, Nils Lukas +2
Obtaining the state of the art performance of deep learning models imposes a high cost to model generators, due to the tedious data preparation and the substantial processing requi…