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Paper Citation Record · LEDGER

Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.08602.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2202.08602 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:25:02.790237Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-18T22:46:53.002183Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4fd14cc4-2f16-49db-9adb-cd87470aa658 · inbound

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking cites this paper.

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-08T17:25:02.790237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:25:02.790237Z digest=sha256:180ce5672961e1a8920d8840d123bd9b7f2d4382ded32342d314bfd6f0152a21

Observation eff67254-6853-4e9c-bf2a-06ad211e2725 · inbound

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

Reference 116

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:46:53.004485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T22:45:31.935618Z digest=sha256:c20b6cff2462c2e3cc0a6d4a2c8570b5e62876a1ce068468e4fc11f42645f8b9