On the Vulnerabilities and Data-Free Removal of Watermarks in Batch Normalization Layers
Batch-normalisation watermarks commonly used for model-ownership verification in federated learning can be stripped without training data, weakening a key IP-protection mechanism for collaborative ML.
Summary written by editorial AI · Source link below
arXiv:2610.28076v1 Announce Type: new 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-ba
Editorial Analysis
Organisations relying on federated-learning watermarks for model IP protection should re-evaluate their robustness given this data-free removal attack.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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