Paper Citation Record · LEDGER

ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2112.02797.

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

pith.paper-citation-record.v1
2112.02797 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:58:20.643127Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T07:20:43.953719Z

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 17ba5133-f359-47b9-b8c4-fe2f1bc452b5 · inbound

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks cites this paper.

SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:20.643127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86ecc305-31b8-4121-9d4d-259de4a5c87f · inbound

Securing Traffic Sign Recognition Systems in Autonomous Vehicles cites this paper.

Securing Traffic Sign Recognition Systems in Autonomous Vehicles ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:58:07.694919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8d81b4c-32bb-4c84-810b-12af71a8b22a · inbound

Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach cites this paper.

Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:20:43.955565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0349b8b3-f12c-4d84-a909-6e8fdd5952fc · inbound

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions cites this paper.

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:57.429986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.