Task-agnostic model fingerprinting has recently gained increasing attention due to its ability to provide a universal framework applicable across diverse model architectures and tasks. The current state-of-the-art method, MetaV, ensures generalization by jointly training a set of fingerprints and a neural-network-based global verifier using two large and diverse model sets: one composed of pirated models (i.e., the protected model and its variants) and the other comprising independently trained models. However, publicly available models are scarce in many real-world domains, and constructing such model sets requires intensive training and massive computational resources, posing a significant barrier to deployment. Reducing the number of models can alleviate the overhead, but increases the risk of overfitting, a problem further exacerbated by MetaV's entangled design, in which all fingerprints and the global verifier are jointly trained. This overfitting issue compromises the generalization capability for verifying unseen models. In this paper, we propose LiteGuard, an efficient task-agnostic fingerprinting framework that attains enhanced generalization while significantly lowering computational cost. Specifically, LiteGuard introduces two key innovations: (i) a checkpoint-based model set augmentation strategy that enriches model diversity by leveraging intermediate model snapshots captured during training of each pirated and independently trained model, thereby alleviating the need to train a large number of such models, and (ii) a local verifier architecture that pairs each fingerprint with a lightweight local verifier, thereby reducing parameter entanglement and mitigating overfitting. Extensive experiments across five representative tasks show that LiteGuard consistently outperforms MetaV in both generalization performance and computational efficiency.

翻译：任务无关模型指纹鉴定因能提供适用于不同模型架构和任务的通用框架而备受关注。当前最先进的方法MetaV通过联合训练一组指纹和基于神经网络的全局验证器实现泛化，该方法依赖两个大型多样化模型集：一个由盗版模型（即受保护模型及其变体）组成，另一个包含独立训练的模型。然而，在许多实际领域公开模型稀缺，构建此类模型集需要大量训练和计算资源，成为部署的重大障碍。减少模型数量可降低开销，但会增加过拟合风险，而MetaV的耦合式设计（所有指纹与全局验证器联合训练）进一步加剧了该问题。这种过拟合缺陷会损害针对未见模型验证的泛化能力。本文提出LiteGuard——一个高效任务无关指纹鉴定框架，在显著降低计算成本的同时实现增强泛化。具体而言，LiteGuard引入两项关键创新：（i）基于检查点的模型集扩充策略，通过利用每个盗版模型和独立训练模型训练过程中的中间模型快照丰富模型多样性，从而减少训练大量模型的需求；（ii）局部验证器架构，为每个指纹配备轻量级局部验证器，降低参数耦合程度并缓解过拟合。在五个代表性任务上的大量实验表明，LiteGuard在泛化性能和计算效率上均一致优于MetaV。