Publications

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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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PredNet experiments used datasets from the NGSS cameras at the Gamma Irradiation Facility at Sandia National Laboratories. PredNet results show containers entering the facility as anomalous and with these results we are now determining the best suited statistics to evaluate the outputs. A statistical evaluation of the number of pixels flagged during the testing for the container entering or exiting the facility shows significant differences between the two directions which is very promising