标题： FPEdit：通过局部知识编辑实现鲁棒的LLM指纹识别 显示英文标题

标题： FPEdit: Robust LLM Fingerprinting through Localized Knowledge Editing

摘要： 大型语言模型代表了在计算、数据和工程专业知识方面的重大投资，使其成为极其宝贵的知识产权资产。 然而，这些AI资产仍可能通过微调或黑盒部署被未经授权地重新分发和商业利用。 当前的指纹技术面临一个根本性的权衡：内在方法需要完整的参数访问权限，而基于后门的技术则使用统计异常的触发器，容易被对手检测和过滤。 为解决这些限制，我们引入了FPEdit，这是一种新颖的知识编辑框架，通过修改模型权重的一个稀疏子集来注入语义连贯的自然语言指纹。 这确保了隐蔽且精确的所有权编码，同时不会损害核心功能。 大量实验表明，FPEdit在全参数微调和参数高效的适应下实现了$95$-$100\%$的指纹保留率，同时在24个下游基准测试中保持性能。 此外，FPEdit在量化、剪枝和随机解码下仍然具有鲁棒性，并且可以使用不到32 GB的GPU内存在10分钟内将10对指纹嵌入LLaMA2-7B，相比现有技术资源需求减少了$70\%$。 这些进展使FPEdit成为第一个同时实现对抗适应鲁棒性、抗检测性和模型实用性保护的指纹技术，为对抗性部署场景中大型语言模型的可靠来源验证提供了一种侵入性最小的解决方案。 显示英文摘要

摘要： Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable intellectual assets. Nevertheless, these AI assets remain vulnerable to unauthorized redistribution and commercial exploitation through fine-tuning or black-box deployment. Current fingerprinting approaches face a fundamental trade-off: intrinsic methods require full parameter access, while backdoor-based techniques employ statistically anomalous triggers easily detected and filtered by adversaries. To address these limitations, we introduce FPEdit, a novel knowledge-editing framework that injects semantically coherent natural language fingerprints by modifying a sparse subset of model weights. This ensures stealthy and precise ownership encoding without degrading the core functionality. Extensive experiments show that FPEdit achieves $95$-$100\%$ fingerprint retention under both full-parameter fine-tuning and parameter-efficient adaptation, while preserving performance on 24 downstream benchmarks. Moreover, FPEdit remains robust under quantization, pruning, and stochastic decoding, and can embed 10 fingerprint pairs into LLaMA2-7B in under 10 minutes using less than 32 GB of GPU memory, a $70\%$ reduction in resource requirements compared to existing techniques. These advances establish FPEdit as the first fingerprinting approach to simultaneously achieve robustness against adaptation, resistance to detection, and preservation of model utility, providing a minimally invasive solution for reliable provenance verification of large language models in adversarial deployment scenarios.