As the volume of satellite imagery has increased, so too has our need to use machine learning models to mine that data for meainingful, decision-relevant information. These models are a critical part of our national security infrastructure, tracking the movement of strategic assets and monitoring the impact of adverserial project implementations all around the globe. Despite this, relatively little is known about the susceptability - and defensability - of this class of model to cyber attacks. The Satellite Data Poisoning Project (SDPP) focuses on this quesiton, testing a range of attack and defense strategies for data poisoning in the context of satellite imagery and deep learning.
Satellite Data Poisoning Project (SDPP)
Exploring the susceptability of satellite imagery to data poisoning cyber attacks.
Summer 2019 to Present
Dan Runfola, Peter Kemper, Ethan Brewer, Yaw Ofori-Addae, Jason Lin, John Hennin, Eric Nubbe
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