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Paper Citation Record · LEDGER

Learning Transferable Adversarial Examples via Ghost Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1812.03413.

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

pith.paper-citation-record.v1
1812.03413 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:19:05.619691Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:28:44.902746Z

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 c98e70ef-c376-4ec8-b6e3-9f825d25a614 · inbound

Transferring Robustness for Graph Neural Network Against Poisoning Attacks cites this paper.

Transferring Robustness for Graph Neural Network Against Poisoning Attacks Learning Transferable Adversarial Examples via Ghost Networks

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-14T12:19:05.619691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:19:05.619691Z digest=sha256:98571accb422a26479bca307afdcbeb3dc9f9eb381d1dd0d892fd4e0a548ef4f

Observation 97e63b91-31cd-4f8b-ba9d-2eb2e9008d2d · inbound

Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness cites this paper.

Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness Learning Transferable Adversarial Examples via Ghost Networks

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:28:44.933758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T10:28:44.191932Z digest=sha256:268fcd578db43db053564aa842468fc57a32c3e43ca32907da776fa4e901e232