Securing Safety-Critical Machine Learning Algorithms - January 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

Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. arXiv:2112.14232 [cs.LG] December 2021.

PUBLIC ACCOMPLISHMENT HIGHLIGHTS

No new data

COMMUNITY ENGAGEMENTS (If applicable)

No new data

EDUCATIONAL ADVANCES (If applicable)

N/A this quarter