Elena Rodríguez-Lois, Fernando Pérez-González · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28076 · open-access PDF
Batch Normalization(BN)层已成为在联邦学习(FL)中嵌入白盒水标的常见选择,为模型所有权验证和参与者识别提供了简单有效的机制。然而,以前的工作侧重于对一般模型修改的可靠性,这使得这种设计的安全影响在很大程度上未被探索。在本文中,我们确定了基于BN的水标记方案的基本漏洞,并提出了一种实际的数据自由攻击,利用它们。
Machine translation (Meta M2M100); the English original follows.
Batch Normalization (BN) layers have become a common choice for embedding white-box watermarks in Federated Learning (FL), providing a simple and effective mechanism for model ownership verification and participant identification. However, previous works have focused on robustness against generic model modifications, leaving the security implications of this design largely unexplored. In this paper, we identify fundamental vulnerabilities of BN-based watermarking schemes and propose a practical data-free attack that exploits them. Experimental results on two representative FL watermarking sche
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