7 citations · 16 across the 4 of their papers we have counts for
4 papers
Machine Learning Model Attribution Challenge
Elizabeth Merkhofer, Deepesh Chaudhari, Hyrum S. Anderson +3
We present the findings of the Machine Learning Model Attribution Challenge. Fine-tuned machine learning models may derive from other trained models without obvious attribution cha…
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
Marissa Dotter, Sherry Xie, Keith Manville +3
Machine Learning (ML) models are known to be vulnerable to adversarial inputs and researchers have demonstrated that even production systems, such as self-driving cars and ML-as-a-…
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
Anneliese Braunegg, Amartya Chakraborty, Michael Krumdick +6
Physical adversarial attacks threaten to fool object detection systems, but reproducible research on the real-world effectiveness of physical patches and how to defend against them…
Overhead Detection: Beyond 8-bits and RGB
Eliza Mace, Keith Manville, Monica Barbu-McInnis +3
This study uses the challenging and publicly available SpaceNet dataset to establish a performance baseline for a state-of-the-art object detector in satellite imagery. Specificall…