[2603.21411] Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach

Computer Science > Cryptography and Security arXiv:2603.21411 (cs) [Submitted on 22 Mar 2026] Title:Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach Authors:Guang Yang, Ziye Geng, Yihang Chen, Changqing Luo View a PDF of the paper titled Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach, by Guang Yang and 3 other authors View PDF HTML (experimental) Abstract:Adversarial-example-based fingerprinting approaches, which leverage the decision boundary characteristics of deep neural networks (DNNs) to craft fingerprints, have proven effective for model ownership protection. However, a fundamental challenge remains unresolved: how far a fingerprint should be placed from the decision boundary to simultaneously satisfy two essential properties, i.e., robustness and uniqueness, for effective and reliable ownership protection. Despite the importance of the fingerprint-to-boundary distance, existing works lack a theoretical solution and instead rely on empirical heuristics, which may violate either robustness or uniqueness properties. We propose AnaFP, an analytical fingerprinting scheme that constructs fingerprints under theoretical guidance. Specifically, we formulate fingerprint generation as 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 stre...