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Paper Citation Record · LEDGER

Deep Learning Model Security: Threats and Defenses

As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.08969.

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

pith.paper-citation-record.v1
2412.08969 v2

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 2026-07-23T06:31:01.910684+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:36:18.728469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T09:54:34.401614Z

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 f1a692df-d60d-413f-8506-cd31bd64a80b · inbound

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions cites this paper.

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions Deep Learning Model Security: Threats and Defenses

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-30T09:54:34.403192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

source=pdf_text observed=2026-06-30T09:54:05.683478Z digest=sha256:ec510c02e599bd4b9a234e5d9c2fc16d7031d9b1430431ea4059592cd6a890f5

Observation 621cf58c-e0d6-42ea-81c3-d49222504b18 · inbound

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift cites this paper.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Deep Learning Model Security: Threats and Defenses

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:4a18d2c0bed1fd5373f193ff998e6955d21d88f5d53e9b5b1a1a6c7634024f4e