5 papers
Code-Level Cost Function Generation for Spatial Image Steganography Using RAG-Enhanced Large Language Models
Yige Wang, Shiqi Yi, Hanzhou Wu
Designing cost functions of adaptive steganography traditionally requires extensive manual tuning, while deep learning methods lack interpretability. Although large language models…
Defining Cost Function of Steganography with Large Language Models
Hanzhou Wu, Yige Wang
In this paper, we make the first attempt towards defining cost function of steganography with large language models (LLMs), which is totally different from previous works that rely…
A Fingerprint for Large Language Models
Zhiguang Yang, Hanzhou Wu
Recent advances confirm that large language models (LLMs) can achieve state-of-the-art performance across various tasks. However, due to the resource-intensive nature of training L…
Rotation, Scale, and Translation Resilient Black-box Fingerprinting for Intellectual Property Protection of EaaS Models
Hongjie Zhang, Zhiqi Zhao, Hanzhou Wu +2
Feature embedding has become a cornerstone technology for processing high-dimensional and complex data, which results in that Embedding as a Service (EaaS) models have been widely…
Yet Another Watermark for Large Language Models
Siyuan Bao, Ying Shi, Zhiguang Yang +2
Existing watermarking methods for large language models (LLMs) mainly embed watermark by adjusting the token sampling prediction or post-processing, lacking intrinsic coupling with…