Group-based Robustness: A General Framework for Customized Robustness in the Real World
Weiran Lin
Network and Distributed System Security (NDSS) Symposium 2024 · Day 1 · Adversarial ML
In an era where machine learning models are increasingly integrated into critical real-world applications, their vulnerability to sophisticated evasion attacks poses a significant threat. Weiran Lin's presentation at the NDSS Symposium, "Group-based Robustness: A General Framework for Customized Robustness in the Real World," addresses a crucial blind spot in the current understanding and measurement of model robustness. The talk argues that conventional metrics—such as benign accuracy, untargeted robustness, and targeted robustness—are often inadequate for capturing the complexity of real-world adversarial threats, which frequently involve misclassifications across *groups* of classes rather than single, isolated instances.