12 papers
KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing
Zhenhua Xu, Xiaoning Tian, Wenjun Zeng +5
Protecting the intellectual property of large language models requires robust ownership verification. Conventional backdoor fingerprinting, however, is flawed by a stealth-robustne…
DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protection
Zhenhua Xu, Yiran Zhao, Mengting Zhong +4
The rapid growth of large language models raises pressing concerns about intellectual property protection under black-box deployment. Existing backdoor-based fingerprints either re…
ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models
Zhenhua Xu, Haobo Zhang, Zhebo Wang +4
Existing invasive (backdoor) fingerprints suffer from high-perplexity triggers that are easily filtered, fixed response patterns exposed by heuristic detectors, and spurious activa…
Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation
Xining Song, Zhihua Wei, Rui Wang +3
Identity, accent, style, and emotions are essential components of human speech. Voice conversion (VC) techniques process the speech signals of two input speakers and other modaliti…
CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor
Zhenhua Xu, Xixiang Zhao, Xubin Yue +3
The widespread deployment of large language models (LLMs) has intensified concerns around intellectual property (IP) protection, as model theft and unauthorized redistribution beco…
MEUV: Achieving Fine-Grained Capability Activation in Large Language Models via Mutually Exclusive Unlock Vectors
Xin Tong, Zhi Lin, Jingya Wang +2
Large language models (LLMs) enforce safety alignment to reliably refuse malicious requests, yet the same blanket safeguards also block legitimate uses in policing, defense, and ot…