Paper Citation Record · LEDGER

Measuring the Transferability of Adversarial Examples

As of 10 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:1907.06291.

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

pith.paper-citation-record.v1
1907.06291 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T21:26:53.670675Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:39:29.511016Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T13:39:29.927714Z

Reference resolution

17 of 17 outbound references displayed

verified exact8
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch9

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dba6a7b7-a9b3-4b1c-8f88-39473116759f · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

Measuring the Transferability of Adversarial Examples Towards Evaluating the Robustness of Neural Networks

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:57.996511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7eae53db-344c-487f-b59c-f293495657f7 · outbound

This paper cites Xception: Deep Learning with Depthwise Separable Convolutions.

Measuring the Transferability of Adversarial Examples Xception: Deep Learning with Depthwise Separable Convolutions

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:57.976807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 355c8901-f124-4507-9efd-6eea63aac39d · outbound

This paper cites Adversarial Attacks Against Medical Deep Learning Systems.

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 2026-08-10T06:31:04.303077+00:00.

Observation 29072e6c-778a-4494-b0c1-91297212fb79 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Measuring the Transferability of Adversarial Examples Deep Residual Learning for Image Recognition

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:58.006569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94d8dc47-8639-48e9-a7ee-0986d7dd2e7d · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Measuring the Transferability of Adversarial Examples Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:57.950418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d3b94e0-2ba0-4c74-b1cd-217de78e68ed · outbound

This paper cites Adversarial examples in the physical world.

Measuring the Transferability of Adversarial Examples Adversarial examples in the physical world

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.990904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93093bc0-fe99-48f1-90f9-c1f96c4117dc · outbound

This paper cites ISBN 3-540- 66722-9.

Measuring the Transferability of Adversarial Examples ISBN 3-540- 66722-9

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T21:29:57.966907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f64f3b1-dc5a-478c-bd1c-c1aff37ba2ac · outbound

This paper cites Network In Network.

Measuring the Transferability of Adversarial Examples Network In Network

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.985763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f5ea9337-a985-4565-8914-c733db3353a7 · outbound

This paper cites Adversarial Robustness Toolbox v1.0.0.

Measuring the Transferability of Adversarial Examples Adversarial Robustness Toolbox v1.0.0

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-24T21:29:57.928299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5314feab-37c8-4d4e-b21b-d72c5f7d22a9 · outbound

This paper cites Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks.

Measuring the Transferability of Adversarial Examples Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.938585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b144a29-ea92-4145-85c8-40b0493b9b0f · outbound

This paper cites Improved Techniques for Training GANs.

Measuring the Transferability of Adversarial Examples Improved Techniques for Training GANs

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:58.001785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67941ab3-72f0-4b3d-8ed8-5da5f379be25 · outbound

This paper cites Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models.

Measuring the Transferability of Adversarial Examples Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-24T21:29:57.961463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed6e61e7-0b8c-4a98-8041-0a4028647375 · outbound

This paper cites One pixel attack for fooling deep neural networks.

Measuring the Transferability of Adversarial Examples One pixel attack for fooling deep neural networks

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T21:29:57.933911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9cafcea-973f-4627-984b-5be9ce834d74 · outbound

This paper cites Going Deeper with Convolutions.

Measuring the Transferability of Adversarial Examples Going Deeper with Convolutions

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.946159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ffe6d8e-4b75-411b-86bb-b9e08b90f701 · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

Measuring the Transferability of Adversarial Examples Rethinking the Inception Architecture for Computer Vision

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.981198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e03d2200-f6c6-46c5-bdf7-daf395d4b50d · outbound

This paper cites Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

Measuring the Transferability of Adversarial Examples Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T21:29:57.972298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe5343ec-f217-4b37-ac51-64825ca98511 · outbound

This paper cites Bovik, Hamid R.

Measuring the Transferability of Adversarial Examples Bovik, Hamid R

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T21:29:57.699068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 5b497488-5429-4674-87d4-09aa7a119f07 · inbound

The Relationship Between Network Similarity and Transferability of Adversarial Attacks cites this paper.

The Relationship Between Network Similarity and Transferability of Adversarial Attacks Measuring the Transferability of Adversarial Examples

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:39:29.930963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.