Paper Citation Record · LEDGER

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2005.06149.

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

pith.paper-citation-record.v1
2005.06149 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 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:46:10.432230Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T13:58:22.160581Z

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 1942516d-8219-4483-94e2-254a4cafa5eb · inbound

GRAND : Graph Reconstruction from potential partial Adjacency and Neighborhood Data cites this paper.

GRAND : Graph Reconstruction from potential partial Adjacency and Neighborhood Data DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T23:46:10.432230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1afd4e9-05ff-4b3c-be82-ff88a4ada5d7 · inbound

On the Adversarial Robustness of Graph Neural Networks with Graph Reduction cites this paper.

On the Adversarial Robustness of Graph Neural Networks with Graph Reduction DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:17:39.524804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b77a5f56-69c7-4c2c-b885-e87499560703 · inbound

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack cites this paper.

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:28:37.414109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 429ff459-dec5-47e0-96d6-451ad9c4b20b · inbound

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting cites this paper.

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:16:02.774036Z

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.

Observation 1e9f4c7b-97d2-4168-a5ca-7f1f85e6bafc · inbound

Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation cites this paper.

Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:08.787969Z

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.

Observation edb51bb5-0b2f-4b47-86d4-cf0f0df51469 · inbound

Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition cites this paper.

Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:22.162363Z

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.