Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks Using Hyperparameter Tuning

In this paper, we present the first detailed analysis of how training hyperparameters---such as learning rate, weight decay, momentum, and batch size---influence robustness against both transfer-based and query-based attacks. Supported by theory and experiments, our study spans a variety of practical deployment settings, including centralized training, ensemble learning, and distributed training. We uncover a striking dichotomy: for transfer-based attacks, decreasing the learning rate significantly enhances robustness by up to 64%. In contrast, for query-based attacks, increasing the learning rate consistently leads to improved robustness by up to 28% across various settings and data distributions. Leveraging these findings, we explore---for the first time---the training hyperparameter space to jointly enhance robustness against both transfer-based and query-based attacks. Our results reveal that distributed models benefit the most from hyperparameter tuning, achieving a remarkable tradeoff by simultaneously mitigating both attack types more effectively than other training setups.