Paper Citation Record · LEDGER

A Comprehensive Review of Adversarial Attacks on Machine Learning

As of 12 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2412.11384.

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

pith.paper-citation-record.v1
2412.11384 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:01:54.407974Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

13 of 13 outbound references displayed

verified exact2
verified fuzzy4
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a64b2f5-ee0f-4b36-bdce-85665cb1874e · outbound

This paper cites Adversarial Robustness Toolbox (ART),.

A Comprehensive Review of Adversarial Attacks on Machine Learning Adversarial Robustness Toolbox (ART),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:54.600828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2fa611b3-dcf6-40dc-b0d8-b957c25cd489 · outbound

This paper cites Intriguing properties of neural networks.

A Comprehensive Review of Adversarial Attacks on Machine Learning Intriguing properties of neural networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.359060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2435796-664b-443f-a545-99f80d236109 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

A Comprehensive Review of Adversarial Attacks on Machine Learning Explaining and Harnessing Adversarial Examples

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.363871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f28775a-c5a0-4938-a24a-2383d56d6e0b · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

A Comprehensive Review of Adversarial Attacks on Machine Learning Towards Evaluating the Robustness of Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.368896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bbb54b9-045b-4f22-a28b-58df6ccc60e5 · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

A Comprehensive Review of Adversarial Attacks on Machine Learning Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.377961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d556637b-c8fa-4cd7-9736-4ae8dea53109 · outbound

This paper cites Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks.

A Comprehensive Review of Adversarial Attacks on Machine Learning Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:01:54.503609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5d8aac70-e62c-4680-aab5-afe8832786d7 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

A Comprehensive Review of Adversarial Attacks on Machine Learning Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.386349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fae178cf-3fa1-4070-bc41-4b9be2ac037f · outbound

This paper cites 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes.

A Comprehensive Review of Adversarial Attacks on Machine Learning 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:01:54.473211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 96440fc7-b3d5-4e70-a75b-56942e9f659b · outbound

This paper cites Reconciling modern machine learning practice and the bias-variance trade-off.

A Comprehensive Review of Adversarial Attacks on Machine Learning Reconciling modern machine learning practice and the bias-variance trade-off

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.393563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf58bd93-4872-4391-b2d9-57266e61e9e0 · outbound

This paper cites Available: https://huggingface.co/facebook/detr-resnet-50.

A Comprehensive Review of Adversarial Attacks on Machine Learning Available: https://huggingface.co/facebook/detr-resnet-50

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:54.587952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 066b75e1-e92e-4d0f-bef0-1db2aae2463c · outbound

This paper cites Available: https://www.kaggle.com/datasets/alincijov/self-driving-cars.

A Comprehensive Review of Adversarial Attacks on Machine Learning Available: https://www.kaggle.com/datasets/alincijov/self-driving-cars

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:54.576470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5579a181-63cd-4ef0-92ec-b4b6c28cf36c · outbound

This paper cites Adversarial Robustness Toolbox v1.0.0,.

A Comprehensive Review of Adversarial Attacks on Machine Learning Adversarial Robustness Toolbox v1.0.0,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:01:54.564051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6faaa9f3-5779-437b-be98-ca0c5c20864a · outbound

This paper cites Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors.

A Comprehensive Review of Adversarial Attacks on Machine Learning Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T15:01:54.407974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.