Paper Citation Record · LEDGER

Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2101.08030.

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

pith.paper-citation-record.v1
2101.08030 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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:03:34.764779Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

14
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6bd8d9f1-56b9-4340-9156-8e0ea73d593e · inbound

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency cites this paper.

Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T19:03:34.764779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d004fd5-9ea8-4ea0-96b2-d3d7b384fc68 · inbound

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs cites this paper.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:40:18.703164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb8f0299-0ab6-4abf-838a-2813a788aa34 · inbound

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data cites this paper.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:35:02.210605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56346855-ecb4-4a9a-a2ad-ef942cfac08e · inbound

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection cites this paper.

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:37:04.893327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f59bd82c-86a8-4d6e-98e8-ffb13362be79 · inbound

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications cites this paper.

Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T11:25:31.137128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0435ace3-8467-411a-bea0-fca9c8e0feeb · inbound

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech cites this paper.

When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:43:31.563202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.