ACL 2025 MainCCF-ACodeBibJingxuan Zhang and Zhenhua Xu (co-first authors), Rui Hu, Wenpeng Xing, Xuhong Zhang, Meng Han

We propose MEraser, a two-phase fine-tuning method that erases backdoor-based fingerprints from LLMs while preserving utility, transferring across models with minimal data and no repeated training.

@inproceedings{zhangMEraserEffectiveFingerprint2025,
  title = {{{MEraser}}: {{An Effective Fingerprint Erasure Approach}} for {{Large Language Models}}},
  booktitle = {Proceedings of the 63rd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Zhang, Jingxuan and Xu, Zhenhua and Hu, Rui and Xing, Wenpeng and Zhang, Xuhong and Han, Meng},
  editor = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  year = 2025,
  pages = {30136--30153},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria},
  doi = {10.18653/v1/2025.acl-long.1455},
  urldate = {2025-11-14},
  isbn = {979-8-89176-251-0}
}

EMNLP 2025 MainCCF-BCodeBibZhenhua Xu, Meng Han, Wenpeng Xing

We propose EverTracer, a gray-box probabilistic fingerprint that leverages calibrated probability shifts from MIA-style memorization to enable stealthy, robust provenance tracing against input and model-level modifications.

@inproceedings{xuEverTracerHuntingStolen2025,
  title = {{{EverTracer}}: {{Hunting Stolen Large Language Models}} via {{Stealthy}} and {{Robust Probabilistic Fingerprint}}},
  booktitle = {Proceedings of the 2025 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Xu, Zhenhua and Han, Meng and Xing, Wenpeng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {7019--7042},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.emnlp-main.358},
  urldate = {2025-11-14},
  isbn = {979-8-89176-332-6}
}

EMNLP 2025 MainCCF-BCodeBibZhenhua Xu, Xixiang Zhao, Xubin Yue, shengwei tian, Changting Lin, Meng Han

We propose CTCC, a rule-driven fingerprint that encodes cross-turn contextual correlations in dialogue to achieve black-box verification with higher stealth and robustness and reduced false positives.

@inproceedings{xuCTCCRobustStealthy2025,
  title = {{{CTCC}}: {{A Robust}} and {{Stealthy Fingerprinting Framework}} for {{Large Language Models}} via {{Cross-Turn Contextual Correlation Backdoor}}},
  booktitle = {Proceedings of the 2025 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Xu, Zhenhua and Zhao, Xixiang and Yue, Xubin and Tian, Shengwei and Lin, Changting and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {6978--7000},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.emnlp-main.356},
  urldate = {2025-11-14},
  isbn = {979-8-89176-332-6}
}

EMNLP 2025 FindingsCCF-BCodeBibZhenhua Xu, Zhaokun Yan, Binhan Xu, Xin Tong, Haitao Xu, Yourong Chen, Meng Han

We propose LoRA-FP, a plug-and-play approach that encodes backdoor fingerprints into LoRA adapters and transfers them to downstream models via parameter fusion, enabling low-cost, robust, and contamination-free fingerprinting.

@inproceedings{xuUnlockingEffectivenessLoRAFP2025,
  title = {Unlocking the {{Effectiveness}} of {{LoRA-FP}} for {{Seamless Transfer Implantation}} of {{Fingerprints}} in {{Downstream Models}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2025},
  author = {Xu, Zhenhua and Yan, Zhaokun and Xu, Binhan and Tong, Xin and Xu, Haitao and Chen, Yourong and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {4302--4312},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.findings-emnlp.230},
  urldate = {2025-11-14},
  isbn = {979-8-89176-335-7}
}

EMNLP 2025 FindingsCCF-BBibXubin Yue and Zhenhua Xu (co-first authors), Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han

We propose PREE, a prefix-enhanced fingerprint editing framework that embeds copyright information as minimal parameter offsets via dual-channel knowledge editing, delivering high trigger precision and strong robustness under incremental fine-tuning and defenses.

@inproceedings{yuePREEHarmlessAdaptive2025,
  title = {{{PREE}}: {{Towards Harmless}} and {{Adaptive Fingerprint Editing}} in {{Large Language Models}} via {{Knowledge Prefix Enhancement}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2025},
  author = {Yue, Xubin and Xu, Zhenhua and Xing, Wenpeng and Yu, Jiahui and Li, Mohan and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {3794--3804},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.findings-emnlp.204},
  urldate = {2025-11-14},
  isbn = {979-8-89176-335-7}
}

ICASSP 2026CCF-BBibZhenhua Xu, Yiran Zhao, Mengting Zhong, Dezhang Kong, Changting Lin, Tong Qiao, Meng Han

We propose DNF, a dual-layer nested fingerprinting framework that couples domain-specific stylistic cues with implicit semantic triggers to embed hierarchical backdoor-based fingerprints into large language models, enabling black-box ownership verification with enhanced stealth and resilience to detection and filtering.

@misc{xu2026dnfduallayernestedfingerprinting,
      title={DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protection},
      author={Zhenhua Xu and Yiran Zhao and Mengting Zhong and Dezhang Kong and Changting Lin and Tong Qiao and Meng Han},
      year={2026},
      eprint={2601.08223},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.08223}, 
}

ICASSP 2026CCF-BCodeBibZhenhua Xu, Haobo Zhang, Zhebo Wang, Qichen Liu, Haitao Xu, Wenpeng Xing, Meng Han

We propose ForgetMark, a targeted unlearning–based fingerprint that encodes model ownership via probabilistic forgetting traces, enabling stealthy and robust black-/gray-box verification with minimal performance impact and low false positives.

@misc{xu2026forgetmarkstealthyfingerprintembedding,
      title={ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models},
      author={Zhenhua Xu and Haobo Zhang and Zhebo Wang and Qichen Liu and Haitao Xu and Wenpeng Xing and Meng Han},
      year={2026},
      eprint={2601.08189},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.08189}, 
}

ICASSP 2026CCF-BBibZhenhua Xu, Xiaoning Tian, Wenjun Zeng, Wenpeng Xing, Tianliang Lu, Gaolei Li, Chaochao Chen, Meng Han

We propose KinGuard, a hierarchical kinship-aware fingerprinting framework that models derivation relationships among LLM variants to enable fine-grained provenance tracing and robust defense against model stealing attacks.

@misc{xu2026kinguardhierarchicalkinshipawarefingerprinting,
      title={KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing}, 
      author={Zhenhua Xu and Xiaoning Tian and Wenjun Zeng and Wenpeng Xing and Tianliang Lu and Gaolei Li and Chaochao Chen and Meng Han},
      year={2026},
      eprint={2601.12986},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.12986}, 
}

SCIENTIA SINICA InformationisSCI Q1/JCR Q1/CCF-AIF=7.6BibZhenhua Xu, Meng Han, Xubin Yue, Wenpeng Xing

We propose InSty, a novel fingerprinting method for LLMs in multi-turn dialogues that embeds cross-granularity (word- and sentence-level) triggers across turns, enabling robust, stealthy, and high-recall IP protection under black-box settings.

@article{xuInStyRobustMultilevel2025,
  title = {{{InSty}}: A Robust Multi-Level Cross-Granularity Fingerprint Embedding Algorithm for Multi-Turn Dialogue in Large Language Models},
  author = {Xu, Zhenhua and Han, Meng and Yue, Xubin and Xing, Wenpeng},
  year = 2025,
  journal = {SCIENTIA SINICA Informationis},
  volume = {55},
  number = {8},
  pages = {1906},
  publisher = {Science China Press},
  issn = {1674-7267},
  doi = {10.1360/SSI-2025-0022}
}