Securing Deep Neural Networks Against Adversarial Attacks through Voltage Overscaling
TimeWednesday, December 8th6:00pm - 7:00pm PST
LocationLevel 2 - Lobby
Event Type
Networking Reception
Work-in-Progress Poster
Virtual Programs
Presented In-Person
DescriptionDeep neural networks (DNNs) are shown to be vulnerable to adversarial attacks. Previously proposed defenses against adversarial attacks require substantial additional overheads, making it challenging to deploy these solutions in devices under power and computational resource constraints, such as embedded systems and the Edge. In this paper, we explore the use of voltage over-scaling (VOS) as a lightweight defense against adversarial attacks. Specifically, we exploit the stochastic timing violations of VOS to implement a moving-target defense for DNNs. Our experimental results demonstrate that VOS offers effective defense, does not require any software/hardware modifications, and offers a reduction in power consumption.