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Paper Citation Record · LEDGER

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2506.11901.

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

pith.paper-citation-record.v1
2506.11901 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:09:14.682526Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 75dbc8d9-006a-4674-b189-034e2e8aa14f · outbound

This paper cites Y olo9000: better, faster,str onger,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Y olo9000: better, faster,str onger,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 244473df-6c7b-4ba6-80da-72ddafddcba0 · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T01:09:13.556768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 000e1e1c-297f-4593-9933-c27f0b81ca65 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Imagenet classification with deep convolutional neural networks,

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b7f1ac91-fce4-43c4-9a73-43e965b52264 · outbound

This paper cites V ery deep convolu-tional networks for large-scale image recognition,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification V ery deep convolu-tional networks for large-scale image recognition,

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ef859cb4-36f4-4aab-b5be-a7fff5cb4cba · outbound

This paper cites The IBM 2015 English Conversational Telephone Speech Recognition System.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification The IBM 2015 English Conversational Telephone Speech Recognition System

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T01:09:13.899452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1e6e1063-a7e9-4e21-85ab-bac63df69a07 · outbound

This paper cites Sequence to Sequence Learning with Neural Networks.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Sequence to Sequence Learning with Neural Networks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T01:09:13.963739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3c09cdc8-4df6-46a7-92c8-1bcdd7621ac8 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Over-the-air deep learning based radio signal classification,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:09:16.013149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a629d9ac-0800-4e73-a370-f088bdecbe57 · outbound

This paper cites Classification of Radio Signals and HF Transmission Modes with Deep Learning.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Classification of Radio Signals and HF Transmission Modes with Deep Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T01:09:14.144097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 049d5f2d-a18b-4674-9fb4-2f1e6a169da8 · outbound

This paper cites Adversarial attacks ondee p-learning based radio signal classification,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Adversarial attacks ondee p-learning based radio signal classification,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:09:15.803912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 55e40459-774e-4f5a-825d-aa328ad2f8fd · outbound

This paper cites Adversarial ex-amples : Attacks and defenses for deep learning,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Adversarial ex-amples : Attacks and defenses for deep learning,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:09:15.640635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8cbc227e-4aa0-4c48-b64a-a20d90c64926 · outbound

This paper cites Universal adversarial perturbations,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Universal adversarial perturbations,

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation d857889e-eab1-4f26-8d6c-3df9a4034509 · outbound

This paper cites Deep neural rejection againstadversarial exampl es,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Deep neural rejection againstadversarial exampl es,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:09:15.279499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 78282c79-0b84-4920-a280-04affb370a4c · outbound

This paper cites The backpropagation algorithm,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification The backpropagation algorithm,

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation e9d56e09-b08b-4b2d-b186-74d0a8aae874 · outbound

This paper cites Radio machine learningdatase t generation with gnu radio,.

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification Radio machine learningdatase t generation with gnu radio,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T01:09:14.960940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Pith citing papers

No inbound Pith citation observations are available.