3 citations · 3 across the 6 of their papers we have counts for
9 papers
Image Compression and Actionable Intelligence With Deep Neural Networks
Matthew Ciolino
If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connect…
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline
Matthew Ciolino, Dominick Hambrick, David Noever
The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decrea…
Color Teams for Machine Learning Development
Josh Kalin, David Noever, Matthew Ciolino
Machine learning and software development share processes and methodologies for reliably delivering products to customers. This work proposes the use of a new teaming construct for…
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models
Josh Kalin, David Noever, Matthew Ciolino +2
Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerab…
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models
Josh Kalin, David Noever, Matthew Ciolino
Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assi…
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks
Matthew Ciolino, Josh Kalin, David Noever
Production machine learning systems are consistently under attack by adversarial actors. Various deep learning models must be capable of accurately detecting fake or adversarial in…