Elena Rodríguez-Lois | Search arXiv

arXiv2026

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

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.

cs.CR↗

arXiv2026

BlackCATT: Black-box Collusion Aware Traitor Tracing in Federated Learning

Federated Learning has been popularized in recent years for applications involving personal or sensitive data, as it allows the collaborative training of machine learning models through local updates at the data-owners' premises, which does not require the sharing of the data itself. Considering the risk of leakage or misuse by any of the data-owners, many works attempt to protect their copyright, or even trace the origin of a potential leak through unique watermarks identifying each participant's model copy. Realistic accusation scenarios impose a black-box setting, where watermarks are typically embedded as a set of sample-label pairs. The threat of collusion, however, where multiple bad actors conspire together to produce an untraceable model, has been rarely addressed, and previous works have been limited to shallow networks and near-linearly separable main tasks. To the best of our knowledge, this work is the first to present a general collusion-resistant embedding method for black-box traitor tracing in Federated Learning: BlackCATT, which introduces a novel collusion-aware embedding loss term and, instead of using a fixed trigger set, iteratively optimizes the triggers to aid convergence and traitor tracing performance. Experimental results confirm the efficacy of the proposed scheme across different architectures and datasets. Furthermore, for models that would otherwise suffer from update incompatibility on the main task after learning different watermarks (e.g., architectures including batch normalization layers), our proposed BlackCATT+FR incorporates functional regularization through a set of auxiliary examples at the aggregator, promoting a shared feature space among model copies without compromising traitor tracing performance.

cs.CR↗