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LiteGuard: Efficient Task-Agnostic Model Fingerprinting with Enhanced Generalization

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
neural network fingerprintingownership verification
Abstract:

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 efforts and massive computational resources, posing a significant barrier to practical 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 leads to compromised generalization capability to verify 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 the training of each pirated and independently-trained model—thereby alleviating the need to train a large number of pirated and independently-trained 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.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes LiteGuard, a task-agnostic model fingerprinting framework designed to improve efficiency and generalization over prior work like MetaV. It resides in the Task-Agnostic Universal Fingerprinting Frameworks leaf, which contains four papers total including the original work. This leaf represents a relatively sparse research direction within the broader taxonomy of 37 papers across multiple branches, suggesting that universal fingerprinting frameworks remain an active but not yet saturated area of investigation.

The taxonomy tree positions this work within Fingerprinting Methodology and Architecture, adjacent to leaves covering feature-based embeddings, behavioral fingerprinting, and intrinsic methods. Neighboring branches address robustness concerns and domain-specific adaptations for LLMs and graph neural networks. LiteGuard's emphasis on reducing computational overhead and model set requirements distinguishes it from sibling papers like UTAF and TMOVF, which prioritize broad applicability, and from MetaV's meta-learning approach. The framework bridges universal verification goals with practical deployment constraints, a gap less explored in adjacent leaves.

Among 21 candidates examined through limited semantic search, none clearly refute the three core contributions: checkpoint-based model set augmentation (10 candidates examined, 0 refutable), local verifier architecture (1 candidate examined, 0 refutable), and the overall LiteGuard framework (10 candidates examined, 0 refutable). The checkpoint augmentation strategy and decoupled local verifier design appear novel within this search scope, though the limited candidate pool means potentially relevant prior work in model augmentation or modular verification architectures may exist beyond the top-K matches retrieved.

Based on the restricted literature search covering 21 candidates, the work appears to introduce distinct technical contributions addressing efficiency bottlenecks in task-agnostic fingerprinting. However, the analysis does not cover exhaustive prior work in adjacent areas such as model compression, meta-learning augmentation strategies, or modular neural verification systems, which may contain overlapping ideas. The novelty assessment reflects what is visible within the examined scope rather than a comprehensive field survey.

Taxonomy

Core-task Taxonomy Papers
37
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: task-agnostic model fingerprinting for ownership verification. The field addresses how to prove ownership of machine learning models without relying on task-specific properties, enabling verification across diverse deployment scenarios. The taxonomy reveals five main branches that capture complementary dimensions of this challenge. Fingerprinting Methodology and Architecture explores foundational techniques ranging from universal frameworks like UTAF[37] and TMOVF[32] that work across tasks, to specialized embedding approaches such as Embedding Passports[3] and gradient-based methods like Gradient Model Fingerprinting[4]. Robustness and Attack Resistance examines how fingerprints withstand adversarial manipulations and model modifications. Task-Specific and Domain-Specialized Fingerprinting investigates adaptations for particular model types, including large language models as in LLM Fingerprint[9] and domain-specific architectures. Federated and Distributed Model Ownership Verification tackles ownership in collaborative training settings through works like FedZKP[6] and FedIPR[17]. Privacy-Preserving and Regulatory Frameworks considers legal compliance and privacy constraints during verification. Recent activity highlights tensions between universality and efficiency. Universal frameworks aim for broad applicability but often face overhead challenges, while lightweight approaches such as LiteGuard[0] prioritize minimal computational cost and fast verification. LiteGuard[0] sits within the Task-Agnostic Universal Fingerprinting Frameworks cluster alongside UTAF[37] and TMOVF[32], yet distinguishes itself by emphasizing resource efficiency over maximal generality. Compared to neighbors like Metav[27], which explores meta-learning for fingerprint generation, LiteGuard[0] focuses on streamlined verification suitable for resource-constrained environments. Meanwhile, PatchFinger[2] demonstrates patch-based localized fingerprinting, contrasting with the holistic embedding strategies of Embedding Passports[3]. Open questions persist around balancing robustness against fine-tuning attacks, maintaining verification speed as models scale, and ensuring fingerprints remain imperceptible while resisting removal attempts across diverse architectures and training paradigms.

Claimed Contributions

Checkpoint-based model set augmentation strategy

The authors propose augmenting the piracy and independence model sets by incorporating intermediate checkpoints saved during model training. This strategy increases model diversity without requiring additional training efforts, thereby enhancing generalization capability at no extra computational cost.

10 retrieved papers
Local verifier architecture

Instead of using a global verifier jointly trained with all fingerprints, the authors introduce a design where each fingerprint is paired with its own lightweight local verifier. Different pairs are optimized independently, substantially reducing the number of jointly trained parameters and mitigating overfitting.

1 retrieved paper
LiteGuard framework

The authors present LiteGuard, a task-agnostic model fingerprinting framework that combines checkpoint-based augmentation and local verifier architecture to achieve enhanced generalization capability and computational efficiency compared to existing methods like MetaV.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Checkpoint-based model set augmentation strategy

The authors propose augmenting the piracy and independence model sets by incorporating intermediate checkpoints saved during model training. This strategy increases model diversity without requiring additional training efforts, thereby enhancing generalization capability at no extra computational cost.

Contribution

Local verifier architecture

Instead of using a global verifier jointly trained with all fingerprints, the authors introduce a design where each fingerprint is paired with its own lightweight local verifier. Different pairs are optimized independently, substantially reducing the number of jointly trained parameters and mitigating overfitting.

Contribution

LiteGuard framework

The authors present LiteGuard, a task-agnostic model fingerprinting framework that combines checkpoint-based augmentation and local verifier architecture to achieve enhanced generalization capability and computational efficiency compared to existing methods like MetaV.

Table of Contents