Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.06302.
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 2026-08-16T10:09:06.635951Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T17:08:52.544248Z
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 336c274a-9b71-4952-b23e-0e5cfa77e606 · inbound
The AI Security Zugzwang cites this paper.
The AI Security Zugzwang Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T17:08:52.658898Z
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.
Observation 931b0589-9b1f-4148-b84a-61b4a1a6b1d8 · inbound
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers cites this paper.
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T10:09:06.635951Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2146dcd4-c8c5-412a-a863-211e5f393f3a · inbound
Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs cites this paper.
Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:45:34.181672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.