- By:
- Brogan, Joel R; Sadovnik, Amir ; Bolme, David S; Young, Steven R; Daw, Arka ; Begoli, Edmon
- Page Number:
- 289-302
- Book Title:
- Adversarial Example Detection and Mitigation Using Machine Learning
- Publication Date:
- February 4, 2026
- Publisher Location:
- Springer Nature, Cham, Switzerland
- View DOI Listing:
- https://doi.org/10.1007/978-3-031-99447-0_21
Abstract
While research in the area of Adversarial AI and Mitigation (AAI&M) has quickly grown, research toward investigating the physical realizability of these methods has lagged behind. AAI&M Research often focuses on constrained or 2-Dimensional scenarios that don’t have direct translation to “in-the-wild” real-world problems. As example, an adversarial patch is often only generated and subsequently re-trained against using a single static image, which does not translate to our 3D constantly-in-motion world. To bridge the significant gap between laboratory experiment and real-world scenario, AAI&M research must dive further into unconstrained, noise-and-pose-invariant approaches to AAI how to secure against it. This chapter will focus on multiple distinct AAI&M scenarios and modeling approaches that mimic the unconstrained environments of the physical world.
