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Paper Citation Record · LEDGER

Impact of Adversarial Attacks on Deep Learning Model Explainability

As of 16 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2412.11119.

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

pith.paper-citation-record.v1
2412.11119 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:20:12.090117Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

6 of 6 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87e01f60-b3b2-44f2-bf22-3faf1b75f97a · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Impact of Adversarial Attacks on Deep Learning Model Explainability TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T15:20:12.063696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:20:12.063696Z digest=sha256:7861d9ed3300d1e08ab81441f10877d8bb5c355c329f8bf6f25b61a82fb87a8a

Observation 3604dd48-17ee-450a-bcbb-8446d0f86294 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Impact of Adversarial Attacks on Deep Learning Model Explainability Explaining and Harnessing Adversarial Examples

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T15:20:12.073964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:20:12.073964Z digest=sha256:caa358b343bf13a857ebe0b9e81d951826d53c75cdd589e2bd74ed98722bedf6

Observation 9019bbda-0a5f-4c12-9074-eb1db55145b6 · outbound

This paper cites an unresolved cited work.

Impact of Adversarial Attacks on Deep Learning Model Explainability Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-11T15:20:12.176817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T15:20:12.078845Z digest=sha256:ec6dcde8bb5c032ab9f92df540d75078265c9d15d69e5628129cf21d420f8265

Observation aafb821c-d9c7-4291-ad16-94af0181757d · outbound

This paper cites How to Manipulate CNNs to Make Them Lie: the GradCAM Case.

Impact of Adversarial Attacks on Deep Learning Model Explainability How to Manipulate CNNs to Make Them Lie: the GradCAM Case

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T15:20:12.090117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:20:12.090117Z digest=sha256:0e32610be2ab43879d104dd2274da25bf4552942a9ec475ff57b40670c09129d

Observation f4210106-1610-4248-93f6-bc5af16135d1 · outbound

This paper cites J., Li, K., & Fei -Fei, L.

Impact of Adversarial Attacks on Deep Learning Model Explainability J., Li, K., & Fei -Fei, L

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:20:12.190184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T15:20:12.069299Z digest=sha256:f22fa4a2e559495572be73e75b4e13a9fd8f25e70d4ed943eeb63589da0d52f6

Observation 6e8a51f0-3837-4f23-a27c-f50eed11122b · outbound

This paper cites Synthetic Benchmarks for Scientific Research in Explainable Machine Learning.

Impact of Adversarial Attacks on Deep Learning Model Explainability Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

Reference 7741

Resolution
unresolved
no resolver link, observed 2026-08-11T15:20:12.084638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:20:12.084638Z digest=sha256:72c81380545e1bbc2565934f6d1e925c0a016fe23c8c636791b43e9960c1a2d9

Pith citing papers

No inbound Pith citation observations are available.