1 citations · 1 across the 2 of their papers we have counts for
4 papers
Model-intrinsic, Weight-level, and Training-free Spectral Fingerprinting for Large Language Models
Yanan He, Yanli Chen, Ling Xiong +2
The increasing release and reuse of Large Language Models (LLMs) as open-weight checkpoints make model fingerprinting important for ownership protection and offline auditing. Exist…
HiMark: Hierarchical and Controllable Multi-bit Watermarking for Large Language Models
Ke Zhu, Yanli Chen, Ling Xiong +2
With the rapid adoption of large language models (LLMs) in text generation applications, it has become increasingly difficult to trace generated content and assign responsibility f…
Beyond QA Matching: Perturbation-Response Fingerprinting via Probability Distributions for Large Language Models
Jichao Zeng, Yanli Chen, Hanzhou Wu
Large language models are often instruction-tuned, specialized, quantized, or otherwise transformed, making fine-grained provenance difficult. In this paper, we introduce BReF, a t…
Reversible Embedding to Covers Full of Boundaries
Hanzhou Wu, Wei Wang, Jing Dong +2
In reversible data embedding, to avoid overflow and underflow problem, before data embedding, boundary pixels are recorded as side information, which may be losslessly compressed.…