Collaborative Research: AF: Small: Exploring the Frontiers of Adversarial Robustness

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

In an age where artificial intelligence (AI) is deeply integrated into our lives yet both cyber and physical threats are ever-present, robustness to adversarial input is critically important for ensuring the reliability, security, and trustworthiness of algorithmic design in the face of intentional attacks and manipulations. Unfortunately, many standard big data algorithms are susceptible to exploitation by adversarial input, resulting in major roadblocks to the widespread deployment of AI in safety-critical domains like healthcare and finance. The goal of this project is to identify and address new emerging directions in adversarial robustness and to develop the fundamental principles underlying vulnerabilities to adversarial input. The project will not only realize new elements of algorithmic design and mathematical tools, but also have immediate impact on the wide-ranging applications of trustworthy artificial intelligence. The research team will involve graduate students in this project and plans two workshops on new directions in adversarial robustness and adaptive data analysis. In addition, the team will initiate outreach with a local high school to mentor students in grades 5-12. The goal of this project is to develop new big data algorithms that are robust to adversarial input. At a high level, the primary focal points of this project are: 1) adversarial robustness in the black-box streaming setting, where an adversary has access to the previous outputs but not the internal states of the algorithm, 2) adversarial robustness in the white-box streaming setting, where an adversary additionally has access to the internal state and previous random bits used by the algorithm, and 3) adaptive data analysis with bounded space. Additionally, the research team will explore and integrate new attack models as they emerge, allowing for continuous adaptation to evolving challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.