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 optimization hyperparameters -- such as learning rate, weight decay, momentum, and batch size -- in...
Abstract
In this paper, we present the first detailed analysis of how optimization 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 optimization hyperparameter design 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.
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Citation
Pascal Zimmer. "Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks using Hyperparameter Tuning." arXiv preprint. 2025-11-17. http://arxiv.org/abs/2511.13654v1
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References
- [1]ResearchCredibility: 9/10Pascal Zimmer. "Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks using Hyperparameter Tuning." arXiv.org. November 17, 2025. Accessed November 18, 2025.
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Original Source
This article is based on Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks using Hyperparameter Tuning (arXiv.org)
