438 citations · 670 across the 14 of their papers we have counts for
4 papers · 1 filter
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems
Edward Chou, Florian Tramèr, Giancarlo Pellegrino
SentiNet is a novel detection framework for localized universal attacks on neural networks. These attacks restrict adversarial noise to contiguous portions of an image and are reus…
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
Florian Tramèr, Pascal Dupré, Gili Rusak +2
Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is…
Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attack…
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…