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Securing AI Testbed (Dioptra) Documentation



Harold Booth, James Glasbrenner, Howard Huang, Cory Miniter, Julian Sexton


The NCCoE has built an experimentation testbed to begin to address the broader challenge of evaluation for attacks and defenses. The testbed aims to facilitate security evaluations of ML algorithms under a diverse set of conditions. To that end, it has a modular design enabling researchers to easily swap in alternative datasets, models, attacks, and defenses. The result is an ability to advance the metrology needed to ultimately help secure our ML-enabled systems. The Documentation includes user guidance for implementation, deployment, development, as well as tutorials and examples.
Dioptra Git Hub Project


Trustworthy AI, Artificial Intelligence, test bed, user guidance, implementation, deploy, user guide, metrics, robustness, resilience, AI security


Booth, H. , Glasbrenner, J. , Huang, H. , Miniter, C. and Sexton, J. (2021), Securing AI Testbed (Dioptra) Documentation, Dioptra Git Hub Project, [online],, (Accessed July 14, 2024)


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Created June 14, 2021, Updated November 29, 2022