Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach
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, a...
| Authors | Guang Yang, Ziye Geng, Yihang Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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