152 citations · 262 across the 7 of their papers we have counts for
3 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…