Functional Subspace Watermarking for Large Language Models
Model watermarking utilizes internal representations to protect the ownership of large language models (LLMs). However, these features inevitably undergo complex distortions during realistic model modifications such as fine-tuning, quantization, or knowledge distillation, making reliable extraction extremely challenging. Despite extensive research on model-side watermarking, existing methods still lack sufficient robustness against parameter-level perturbations. To address this gap, we propose \texttt{\textbf{Functional Subspace Watermarking (FSW)}}, a framework that anchors ownership signals into a low-dimensional functional backbone. Specifically, we first solve a generalized eigenvalue...
| Authors | Zikang Ding, Junhao Li, Suling Wu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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