
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 LLMs from scratch, it is urgent and crucial to protect the intellectual property of LLMs against infringement. This has motivated the authors in this paper to propose a novel black-box fingerprinting technique for LLMs. We firstly demonstrate that the outputs of LLMs span a unique vector space associated with each model. We model the problem of fingerprint authentication as the task of evaluating the similarity between the space of the victim model and the space of the suspect model. To tackle with this problem, we introduce two solutions: the first determines whether suspect outputs lie within the victim’s subspace, enabling fast infringement detection; the second reconstructs a joint subspace to detect models modified via parameter-efficient fine-tuning (PEFT). Experiments indicate that the proposed method achieves superior performance in fingerprint verification and robustness against the PEFT attacks. This work reveals inherent characteristics of LLMs and provides a promising solution for protecting LLMs, ensuring efficiency, generality and practicality.

This work shows a fingerprint method for the unique identification of blank and printed paper by a smartphone. This allows a secure authentication by authorities or end users of products or documents. The digital file includes no hidden data. The fingerprint method uses uncontrollable printing variabilities and paper structure as features. The uncontrollable variabilities are mapped into a binary sequnce, which is used as representation of the features and acts as our fingerprint. The variabilities can be extracted from low and high quality paper as well as from printed material created with low-cost office printers and high-end offset printing machines. Based on this fingerprint, various applications can be realized where the distinction between original and copy or forgery is essential, such as piracy of packaging, tickets, coupons or official documents. From the results of the evaluation it can be concluded that the proposed method is independent of the smartphones used, the paper, the printing technology and the color temperature of the ambient light. Furthermore, the test results show that the proposed method works robustly at different distances, from the smartphone camera to the paper.