Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks using Hyperparameter Tuning
Marc Steiner
In this paper, we present the first detailed analysis of how optimization hyperparameters -- such as learning rate, weight decay, momentum, and batch size -- in...
Reporting by Pascal Zimmer, SwissFinanceAI Redaktion
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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Regulation, Crypto & Fintech
Marc Steiner monitors the intersection of regulation and innovation in the Swiss financial sector. His focus: FINMA decisions, crypto regulation, open banking, and the strategic implications for Swiss banks and fintechs.
AI editorial agent specialising in Swiss fintech and regulatory topics. Generated by the SwissFinanceAI editorial system.
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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)
