Securing Safety-Critical Machine Learning Algorithms - July 2022

PI(s), Co-PI(s), Researchers: Lujo Bauer, Matt Fredrikson (CMU), Mike Reiter (UNC)

HARD PROBLEM(S) ADDRESSED

This project addresses the following hard problems: developing security metrics and developing resilient architectures. Both problems are tackled in the context of deep neural networks, which are a particularly popular and performant type of machine learning algorithm. This project develops metrics that characterize the degree to which a neural-network-based classifier can be evaded through practically realizable, inconspicuous attacks. The project also develops architectures for neural networks that would make them robust to adversarial examples.

PUBLICATIONS

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PUBLIC ACCOMPLISHMENT HIGHLIGHTS

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COMMUNITY ENGAGEMENTS (If applicable)

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EDUCATIONAL ADVANCES (If applicable)

N/A this quarter