Paper Citation Record · LEDGER

Adversarial Attacks Against Medical Deep Learning Systems

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1804.05296.

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

pith.paper-citation-record.v1
1804.05296 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:41:49.677370Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-24T21:29:57.952669Z

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 355c8901-f124-4507-9efd-6eea63aac39d · inbound

Measuring the Transferability of Adversarial Examples cites this paper.

Measuring the Transferability of Adversarial Examples Adversarial Attacks Against Medical Deep Learning Systems

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:57.955681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abfd2c4c-8145-45fd-8a85-038f1480a197 · inbound

DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation cites this paper.

DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation Adversarial Attacks Against Medical Deep Learning Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T13:22:56.363578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55e0964a-36e8-4f90-a38c-8376731a9871 · inbound

Parametric Majorization for Data-Driven Energy Minimization Methods cites this paper.

Parametric Majorization for Data-Driven Energy Minimization Methods Adversarial Attacks Against Medical Deep Learning Systems

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-14T13:03:36.619538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9509c0c2-7dde-4121-a4fc-e9276506aaa0 · inbound

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation cites this paper.

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation Adversarial Attacks Against Medical Deep Learning Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:40.052784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82780489-d8e9-4442-b440-517a61529ca3 · inbound

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription cites this paper.

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription Adversarial Attacks Against Medical Deep Learning Systems

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:08.392145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62d2fd2d-bf89-4492-be6e-afdb98bf0e34 · inbound

Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation cites this paper.

Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation Adversarial Attacks Against Medical Deep Learning Systems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T00:41:49.677370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5533e13-f492-4e5d-930a-c2a08fcf543b · inbound

Black-box Adversarial Attacks on CNN-based SLAM Algorithms cites this paper.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Adversarial Attacks Against Medical Deep Learning Systems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:18:50.673947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba142376-234d-4930-adb2-70c4105cf77a · inbound

Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices cites this paper.

Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices Adversarial Attacks Against Medical Deep Learning Systems

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T19:49:53.323459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac982b30-8266-43eb-86e8-78a1b0530cea · inbound

Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks cites this paper.

Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks Adversarial Attacks Against Medical Deep Learning Systems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:04.130788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.