Paper Citation Record · LEDGER

ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:51:11.116814Z

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 e604c534-3a77-4f20-8921-69bbc5083b08 · inbound

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack cites this paper.

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T18:00:27.280507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f587520f-fdc2-42d1-8e54-fc719012ce06 · inbound

Algorithmic Collective Action with Two Collectives cites this paper.

Algorithmic Collective Action with Two Collectives ML Attack Models: Adversarial Attacks and Data Poisoning Attacks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T04:51:11.116814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.