491 citations · 652 across the 15 of their papers we have counts for
6 papers · 1 filter
Separation of Powers in Federated Learning
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu +4
Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggrega…
Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attack…
Tyche: Risk-Based Permissions for Smart Home Platforms
Amir Rahmati, Earlence Fernandes, Kevin Eykholt +1
Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smart…
Note on Attacking Object Detectors with Adversarial Stickers
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable…
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. G…
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
Earlence Fernandes, Amir Rahmati, Kevin Eykholt +1
The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into…