208 citations · 588 across the 22 of their papers we have counts for
4 papers · 1 filter
Learning Universal Adversarial Perturbations with Generative Models
Jamie Hayes, George Danezis
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a miscla…
LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes, Luca Melis, George Danezis +1
Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inf…
The Loopix Anonymity System
Ania Piotrowska, Jamie Hayes, Tariq Elahi +2
We present Loopix, a low-latency anonymous communication system that provides bi-directional 'third-party' sender and receiver anonymity and unobservability. Loopix leverages cover…
Generating Steganographic Images via Adversarial Training
Jamie Hayes, George Danezis
Adversarial training was recently shown to be competitive against supervised learning methods on computer vision tasks, however, studies have mainly been confined to generative tas…