arXiv cs.AI· Bingxue Zhang, Xiaofeng Xu, Feida Zhu·· 13 小时前AI 评分10
LineageMark：面向模型衍生链贡献追踪的多用户白盒水印框架
LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains
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该论文已被作者 Xiaofeng Xu 撤回，原因是研究在问题设定、范围和实验评估上均有大幅修改，当前版本已不能代表修订后的研究。原文提出 LineageMark 多用户白盒水印框架，通过基于投影的方法在模型参数中编码水印，先选取稳定载体以降低对模型变化的敏感性，再将每个水印比特表示为载体上的投影统计量。
正文 · 原文
This paper has been withdrawn by Xiaofeng Xu
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Abstract:In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains. Consequently, tracking and verifying historical contributions is essential for model provenance and intellectual property protection. However, existing watermarking methods are mainly designed for single-user, one-time embeddings, often fail under repeated model derivation and incremental updates. To address this problem, we propose LineageMark, a multi-user white-box watermarking framework for model derivation chains. The framework encodes watermarks in model parameters using a projection-based approach. Stable carriers are first selected to reduce sensitivity to model changes, each watermark bit is then represented as a projection statistic over these carriers. Additional watermark insertions introduce only bounded perturbations in the projection space, and margin constraints are used to maintain signal integrity. We evaluate the effectiveness of LineageMark in multi-stage model derivation chains. Experimental results show that LineageMark preserves contributor watermarks across multi-stage derivation and supports incremental multi-user watermark insertion. Furthermore, it exhibits robustness against perturbations such as re-watermarking, fine-tuning, quantization, and pruning.
| Comments: | The authors have decided to withdraw this manuscript because the research has undergone substantial revisions in its problem formulation, scope, and experimental evaluation. As a result, the current version no longer adequately represents the revised study. We therefore withdraw this earlier version to avoid potential misunderstandings regarding the research findings |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.17123 [cs.CR] |
| (or arXiv:2606.17123v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17123 arXiv-issued DOI via DataCite |
Submission history
From: Xiaofeng Xu [view email]
[v1]
Mon, 15 Jun 2026 13:43:43 UTC (371 KB)
[v2]
Tue, 27 Oct 2026 14:37:50 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org