OpenNovelty.org

Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach

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

Adversarial-example-based fingerprinting approaches, which leverage the decision boundary characteristics of deep neural networks (DNNs) to craft fingerprints, has proven effective for protecting model ownership. However, a fundamental challenge remains unresolved: how far a fingerprint should be placed from the decision boundary to simultaneously satisfy two essential properties—robustness and uniqueness—required for effective and reliable ownership protection. Despite the importance of the fingerprint-to-boundary distance, existing works offer no theoretical solution and instead rely on empirical heuristics to determine it, which may lead to violations of either robustness or uniqueness properties.

We propose AnaFP, an analytical fingerprinting scheme that constructs fingerprints under theoretical guidance. Specifically, we formulate the fingerprint generation task as the problem of controlling the fingerprint-to-boundary distance through a tunable stretch factor. To ensure both robustness and uniqueness, we mathematically formalize these properties that determine the lower and upper bounds of the stretch factor. These bounds jointly define an admissible interval within which the stretch factor must lie, thereby establishing a theoretical connection between the two constraints and the fingerprint-to-boundary distance. To enable practical fingerprint generation, we approximate the original (infinite) sets of pirated and independently trained models using two finite surrogate model pools and employ a quantile-based relaxation strategy to relax the derived bounds. Particularly, due to the circular dependency between the lower bound and the stretch factor, we apply a grid search strategy over the admissible interval to determine the most feasible stretch factor. Extensive experimental results demonstrate that AnaFP consistently outperforms prior methods, achieving effective and reliable ownership verification across diverse model architectures and model modification attacks.

Disclaimer
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 AnaFP, an analytical fingerprinting scheme that theoretically determines fingerprint-to-boundary distance through a tunable stretch factor. It resides in the 'DeepFool and Classification Boundary Fingerprinting' leaf, which contains four papers total, including the original work. This leaf sits within the broader 'Adversarial Perturbation-Based Fingerprinting' branch, indicating a moderately populated research direction focused on leveraging decision boundaries for ownership verification. The taxonomy shows this is an active but not overcrowded subfield, with sibling approaches exploring universal perturbations and adversarial trajectories in parallel.

The paper's leaf neighbors include 'Universal Adversarial Perturbation Fingerprinting' and 'Adversarial Trajectory Fingerprinting', both exploring alternative adversarial strategies for model characterization. The broader 'Fingerprinting-Based Ownership Verification' branch contrasts with 'Watermarking-Based' methods that modify training, highlighting AnaFP's non-invasive positioning. Related work in 'Membership Inference' and 'Meta-Training' fingerprinting diverges by exploiting privacy leakage or inner decision areas rather than boundary geometry. The taxonomy structure suggests AnaFP bridges theoretical analysis with practical boundary-based fingerprinting, occupying a niche between purely empirical methods and domain-agnostic frameworks.

Among twenty-three candidates examined, none clearly refute the three core contributions. The analytical distance-control framework examined ten candidates with zero refutations, suggesting limited prior work on theoretical boundary-distance guidance. The mathematical formalization of robustness-uniqueness bounds reviewed seven candidates without overlap, indicating novelty in constraint derivation. The practical generation scheme using surrogate pools assessed six candidates, also finding no direct precedents. These statistics reflect a focused search scope rather than exhaustive coverage, but within this limited examination, the theoretical framing of fingerprint placement appears distinctive compared to existing empirical heuristics in sibling papers.

Based on top-twenty-three semantic matches, the work appears to introduce theoretical rigor to a subfield previously dominated by empirical tuning. The absence of refutations across contributions suggests the analytical approach to boundary-distance control is underexplored in examined literature. However, the limited search scope means potential overlaps in broader adversarial fingerprinting research or recent preprints may exist beyond this analysis. The taxonomy context confirms the paper targets a specific gap—theoretical foundations for boundary-based fingerprinting—within an active but not saturated research area.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: fingerprinting deep neural networks for ownership protection. The field has evolved into several major branches that address different aspects of model ownership verification. Watermarking-based approaches embed hidden signals directly into model parameters or outputs, often relying on trigger sets or backdoor patterns to prove ownership. Fingerprinting-based methods instead exploit inherent model behaviors—such as adversarial perturbations or decision boundaries—to generate unique identifiers without modifying training data. Domain-specific branches tailor these techniques to specialized architectures like graph neural networks or federated learning environments, while survey literature synthesizes emerging trends and robustness challenges. Representative works such as DeepSigns[25] and DeepMarks[34] illustrate early watermarking strategies, whereas DeepFool Fingerprinting[2] and IPGuard Boundary[8] exemplify adversarial fingerprinting that leverages classification boundaries. A particularly active line of research focuses on adversarial perturbation-based fingerprinting, where methods generate minimal perturbations that reveal model-specific decision surfaces. Within this cluster, DeepFool Fingerprinting[2] and IPGuard Fingerprinting[24] use boundary-proximity techniques to create robust fingerprints, while Universal Adversarial Fingerprinting[3] extends the idea to universal perturbations that generalize across inputs. Fingerprinting Analytical[0] sits squarely in this adversarial boundary fingerprinting subfield, sharing the emphasis on classification boundaries with IPGuard Boundary[8] and DeepFool Fingerprinting[2], yet it appears to offer a more analytical framework for understanding how these perturbations encode ownership information. Compared to IPGuard Fingerprinting[24], which prioritizes practical verification protocols, Fingerprinting Analytical[0] likely delves deeper into the theoretical underpinnings of boundary-based fingerprints. Meanwhile, recent works like DeepVerifier[5] and PreGIP[4] explore complementary directions—such as verifiable inference and generative fingerprinting—highlighting ongoing debates about trade-offs between robustness, stealthiness, and computational overhead in ownership protection schemes.

Claimed Contributions

Analytical fingerprinting scheme with theoretical guidance for fingerprint-to-boundary distance

The authors introduce AnaFP, a fingerprinting method that theoretically determines how far fingerprints should be placed from decision boundaries. This is achieved by formulating fingerprint generation as controlling the fingerprint-to-boundary distance via a tunable stretch factor, addressing a fundamental challenge left unresolved by prior empirical approaches.

10 retrieved papers
Mathematical formalization of robustness and uniqueness constraints with derived bounds

The authors mathematically formalize the robustness and uniqueness properties required for effective fingerprints, deriving lower and upper bounds for the stretch factor. These bounds define an admissible interval that theoretically connects the two essential constraints to the fingerprint-to-boundary distance.

7 retrieved papers
Practical fingerprint generation using surrogate model pools and quantile-based relaxation

The authors develop a practical implementation approach that approximates the theoretically infinite sets of pirated and independently trained models with finite surrogate pools. They employ quantile-based relaxation to make the theoretical bounds feasible and use grid search to handle circular dependencies in determining the stretch factor.

6 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Analytical fingerprinting scheme with theoretical guidance for fingerprint-to-boundary distance

The authors introduce AnaFP, a fingerprinting method that theoretically determines how far fingerprints should be placed from decision boundaries. This is achieved by formulating fingerprint generation as controlling the fingerprint-to-boundary distance via a tunable stretch factor, addressing a fundamental challenge left unresolved by prior empirical approaches.

Contribution

Mathematical formalization of robustness and uniqueness constraints with derived bounds

The authors mathematically formalize the robustness and uniqueness properties required for effective fingerprints, deriving lower and upper bounds for the stretch factor. These bounds define an admissible interval that theoretically connects the two essential constraints to the fingerprint-to-boundary distance.

Contribution

Practical fingerprint generation using surrogate model pools and quantile-based relaxation

The authors develop a practical implementation approach that approximates the theoretically infinite sets of pirated and independently trained models with finite surrogate pools. They employ quantile-based relaxation to make the theoretical bounds feasible and use grid search to handle circular dependencies in determining the stretch factor.

Table of Contents