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On the Vulnerabilities and Data-Free Removal of Watermarks in Batch Normalization Layers

Elena Rodríguez-Lois, Fernando Pérez‐González · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28076 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Batch Normalization (BN) レイヤーは、Federated Learning (FL) でホワイトボックスのウォーターマークを組み込むための一般的な選択肢となり、モデル所有権検証と参加者識別のためのシンプルかつ効果的なメカニズムを提供しています。しかし、以前の作業は、一般的なモデル変更に対する強度に焦点を当てて、このデザインのセキュリティの影響をほとんど未調査に残しています。この論文では、BN ベースのウォーターマークシステムの根本的な脆弱性を特定し、それらを悪用する実用的なデータフリー攻撃を提案しました。二つの代表的な FL ウォーターマークシステムの実験結果は、提案された攻撃がモデルのパフォーマンスに最小限の影響を及

Machine translation (Meta M2M100); the English original follows.

Abstract

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 schemes show that the proposed attack consistently removes the watermark with minimal impact on model performance, even without access to representative training data. More broadly, our findings highlight the importance of evaluating DNN watermarking schemes under threat models consistent with Kerckhoffs's principle, rather than relying on security through obscurity.

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Topics: Digital watermarking, Normalization (sociology), Computer science, Robustness (evolution), Exploit, Embedding

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