2 papers
cs.CR2019
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
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…