152 citations · 177 across the 6 of their papers we have counts for
4 papers · 1 filter
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning
Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber +1
DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim.…
Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks
Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber +1
Deep neural network (DNN) classifiers are powerful tools that drive a broad spectrum of important applications, from image recognition to autonomous vehicles. Unfortunately, DNNs a…
HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks
Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber +1
Deep Neural Networks (DNNs) are employed in an increasing number of applications, some of which are safety critical. Unfortunately, DNNs are known to be vulnerable to so-called adv…
SPEECHMINER: A Framework for Investigating and Measuring Speculative Execution Vulnerabilities
Yuan Xiao, Yinqian Zhang, Radu Teodorescu
SPEculative Execution side Channel Hardware (SPEECH) Vulnerabilities have enabled the notorious Meltdown, Spectre, and L1 terminal fault (L1TF) attacks. While a number of studies h…