Group-based Robustness: A General Framework for Customized Robustness in the Real World
This work addresses the need for more accurate robustness evaluation in specific threat models for machine learning security, though it appears incremental as it builds on existing robustness frameworks.
The paper tackles the problem of evaluating machine-learning model robustness in real-world attack scenarios where existing metrics fail, by introducing a new metric called group-based robustness and demonstrating it with empirical gains, such as up to 99% time savings in attack strategies and up to 3.52x improvement in defense.
Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where the true threat cannot be assessed accurately by existing attacks. Specifically, we find that conventional metrics measuring targeted and untargeted robustness do not appropriately reflect a model's ability to withstand attacks from one set of source classes to another set of target classes. To address the shortcomings of existing methods, we formally define a new metric, termed group-based robustness, that complements existing metrics and is better-suited for evaluating model performance in certain attack scenarios. We show empirically that group-based robustness allows us to distinguish between models' vulnerability against specific threat models in situations where traditional robustness metrics do not apply. Moreover, to measure group-based robustness efficiently and accurately, we 1) propose two loss functions and 2) identify three new attack strategies. We show empirically that with comparable success rates, finding evasive samples using our new loss functions saves computation by a factor as large as the number of targeted classes, and finding evasive samples using our new attack strategies saves time by up to 99\% compared to brute-force search methods. Finally, we propose a defense method that increases group-based robustness by up to 3.52$\times$.