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Paper Citation Record · LEDGER

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.06796.

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

pith.paper-citation-record.v1
1901.06796 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:39:29.563385Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:07:00.102841Z

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 c7a3085c-aac9-40c1-a77b-6f3a3d818f03 · inbound

The Relationship Between Network Similarity and Transferability of Adversarial Attacks cites this paper.

The Relationship Between Network Similarity and Transferability of Adversarial Attacks Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-10T13:39:29.563385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:39:29.563385Z digest=sha256:5337413979dd4fd20adac0b719b627505db6b7f52822f0526e1716d7e52a06b5

Observation 0dc3ffe2-6c99-471d-878c-0087a82c812f · inbound

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks cites this paper.

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

Reference 40

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T04:07:00.107270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T04:07:00.055376Z digest=sha256:3c7d60ecaf50cb6930f715365473ad0726e2dfd528109d9f6034e40e7e44ae0c