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ARXIV:2603.21411 · AI SECURITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21411AI SECURITYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEGuang Yang · Ziye Geng · Yihang Chen · Changqing Luo · arXiv
An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary.
Opportunity summary
Pain An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary. However, a fundamental challenge remains unresolved: how far a fingerprint should be…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To enable practical fingerprint generation, we approximate the original (infinite) sets of pirated and independently trained models using two finite surrogate model pools and…
AI Security moved forward this cycle; last verified April 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary.
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Paper Pack
10.48550/arXiv.2603.21411An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary.
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 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. Due to the circular dependency between the lower bound and the stretch factor, we apply grid search over the admissible interval to determine the most feasible stretch factor. Extensive experimental results show that AnaFP consistently outperforms prior methods, achieving effective ownership verification across diverse model architectures and model modification attacks.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary. However, a fundamental challenge remains unresolved: how far a fingerprint should be placed from the decision...
METHOD
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 fi...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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 relax...
WHY NOW
AI Security moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Security moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An analytical approach to fingerprinting deep neural networks for model ownership protection by mathematically defining the optimal distance from the decision boundary.
Segment
AI Security
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
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CITED BY
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Defensibility
missing
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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missing
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Evidence
Build Passport ledger does not include regulatory flags.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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