250 citations · 863 across the 66 of their papers we have counts for
7 papers · 1 filter
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit
Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
Machine learning models are critically susceptible to evasion attacks from adversarial examples. Generally, adversarial examples, modified inputs deceptively similar to the origina…
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Bao Gia Doan, Minhui Xue, Shiqing Ma +2
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the model's decision. We expose the existence of an intrigu…
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables
Alireza Abedin, Hamid Rezatofighi, Damith C. Ranasinghe
Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and tim…
The Megopolis Resampler: Memory Coalesced Resampling on GPUs
Joshua A. Chesser, Hoa Van Nguyen, Damith C. Ranasinghe
The resampling process employed in widely used methods such as Importance Sampling (IS), with its adaptive extension (AIS), are used to solve challenging problems requiring approxi…
NoisFre: Noise-Tolerant Memory Fingerprints from Commodity Devices for Security Functions
Yansong Gao, Yang Su, Surya Nepal +1
Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Inte…
ReaDmE: Read-Rate Based Dynamic Execution Scheduling for Intermittent RF-Powered Devices
Yang Su, Damith C. Ranasinghe
This paper presents a method for remotely and dynamically determining the execution schedule of long-running tasks on intermittently powered devices such as computational RFID. Our…