Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1801.04354.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:18:47.253589Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T10:46:06.375198Z
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 5801387a-369e-43e0-92e6-cc7bc373fba4 · inbound
GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge cites this paper.
GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:18:47.253589Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f514851b-6f2f-4822-9efa-cce1f3839232 · inbound
WAFBOOSTER: Automatic Boosting of WAF Security Against Mutated Malicious Payloads cites this paper.
WAFBOOSTER: Automatic Boosting of WAF Security Against Mutated Malicious Payloads Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:38:23.765873Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 429a887c-8a2d-4c57-a4f8-d19d71eb1aed · inbound
Analyzing the Effect of Noise in LLM Fine-tuning cites this paper.
Analyzing the Effect of Noise in LLM Fine-tuning Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T22:47:45.878803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.