438 citations · 690 across the 7 of their papers we have counts for
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stat.ML2018
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramèr, Dan Boneh
As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solut…
stat.ML2017★ 438 cited
The Space of Transferable Adversarial Examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow +2
Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more th…