190 citations · 316 across the 24 of their papers we have counts for
4 papers · 1 filter
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Ali Shahin Shamsabadi, Francisco Sepúlveda Teixeira, Alberto Abad +3
Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification. In this work, we propose a white-…
Exploiting vulnerabilities of deep neural networks for privacy protection
Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi +2
Adversarial perturbations can be added to images to protect their content from unwanted inferences. These perturbations may, however, be ineffective against classifiers that were n…
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +4
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neur…
PrivEdge: From Local to Distributed Private Training and Prediction
Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi +1
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user…