College - Author 1
College of Engineering
Department - Author 1
Mechanical Engineering Department
College - Author 2
College of Science and Mathematics
Department - Author 2
Mathematics Department
Advisor
Dr. Brendon Anderson, College of Engineering, Mechanical Engineering
Funding Source
Cal Poly’s Mechanical Engineering Department
Date
10-2025
Abstract/Summary
Modern artificial intelligence (AI) systems exhibit highly sensitive and unsafe behavior when subjected to undetectable cyberattacks. For instance, human-imperceptible manipulations of the pixels in image data can cause traffic sign classifiers to mispredict stop signs as yield signs. In this project, we will design and analyze new methods to robustify machine learning (ML) models against these adversarial threats. Specifically, we will explore randomization techniques that "smooth out" the ML model's decision making process by intentionally corrupting input data with small amounts of noise. Optimizing this noise to enhance resilience against attacks while maintaining the system's accuracy poses a major open problem that we will pursue. The end goal of this project is not only to design and implement such a randomized smoothing method that increases the state-of-the-art robustness of ML systems, but also to develop safety certificates of the proposed method using mathematical analysis. This project is inherently cross-disciplinary, drawing on tools from mechatronics and autonomous systems, computer science and programming, and mathematics. As such, all students with backgrounds and interests in any of these areas are encouraged to apply.
October 1, 2025.
Included in
URL: https://digitalcommons.calpoly.edu/ceng_surp/171