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Safeguards-Informed Hybrid Imagery Dataset [Poster]

Rutkowski, Joshua E.; Gastelum, Zoe N.; Shead, Timothy M.; Rushdi, Ahmad R.; Bolles, Jason C.; Mattes, Arielle M.

Deep Learning computer vision models require many thousands of properly labelled images for training, which is especially challenging for safeguards and nonproliferation, given that safeguards-relevant images are typically rare due to the sensitivity and limited availability of the technologies. Creating relevant images through real-world staging is costly and limiting in scope. Expert-labeling is expensive, time consuming, and error prone. We aim to develop a data set of both realworld and synthetic images that are relevant to the nuclear safeguards domain that can be used to support multiple data science research questions. In the process of developing this data, we aim to develop a novel workflow to validate synthetic images using machine learning explainability methods, testing among multiple computer vision algorithms, and iterative synthetic data rendering. We will deliver one million images – both real-world and synthetically rendered – of two types uranium storage and transportation containers with labelled ground truth and associated adversarial examples.

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TYPE Conference Poster YEAR 2021
OSTIDOI
11 Results
11 Results