Paper Citation Record · LEDGER

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.21483.

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

pith.paper-citation-record.v1
2507.21483 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:47:08.051733Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

39 of 39 outbound references displayed

verified exact0
verified fuzzy27
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb8f4ca3-c0e7-499f-9296-cebbcb6ab6a2 · outbound

This paper cites Audiomnist: exploring explainable artificial intelligence for audio analysis on a simple benchmark.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Audiomnist: exploring explainable artificial intelligence for audio analysis on a simple benchmark

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:14.676876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7bd1656a-c1f9-48a9-bf95-f025df6f74ad · outbound

This paper cites Towards evaluating the robustness of neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Towards evaluating the robustness of neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:14.529630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 580c005e-a314-44a0-8e8c-0420a40ba7ac · outbound

This paper cites Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:04.755848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7f00ccc-d598-4dd8-8758-41d145e2a37a · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:04.847418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45bcc259-2538-479d-81b3-9273fb14eafb · outbound

This paper cites On the detection of adaptive adversarial attacks in speaker verification systems.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples On the detection of adaptive adversarial attacks in speaker verification systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:14.259451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5866b18f-5bb7-4bcc-b6fb-22434eb8d2ca · outbound

This paper cites Front- end factor analysis for speaker verification.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Front- end factor analysis for speaker verification

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:13.993344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b48ff91-eb56-4605-8498-597093bf40e4 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Imagenet: A large-scale hierarchical image database

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:05.083415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312c2a71-b320-4db8-bfd9-df9854f53e26 · outbound

This paper cites Formal verification of piece-wise linear feed-forward neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Formal verification of piece-wise linear feed-forward neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:13.736843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e88d2846-4ba1-4623-89c2-902a2576bef5 · outbound

This paper cites Strip: A defence against trojan attacks on deep neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Strip: A defence against trojan attacks on deep neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:13.539367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8a5eaf86-7c58-42c7-9da5-6d9228c56626 · outbound

This paper cites Ai2: Safety and robustness certification of neural networks with abstract inter- pretation.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Ai2: Safety and robustness certification of neural networks with abstract inter- pretation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:13.235951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4db5ee45-013c-418f-a381-093ac71b9182 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Explaining and Harnessing Adversarial Examples

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:05.394682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f9cff4-f492-4dcc-9f89-f737e43ffa82 · outbound

This paper cites Badnets: Evaluating back- dooring attacks on deep neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Badnets: Evaluating back- dooring attacks on deep neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:12.919523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bec7e872-d041-4723-9d52-8209101cd799 · outbound

This paper cites an unresolved cited work.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:47:12.667634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ff4d59c-ab42-4f41-af7f-6d2b1c0769fd · outbound

This paper cites Deep residual learning for image recognition.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Deep residual learning for image recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:05.655425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34eba88-c43f-4aa9-86e7-ef0b11a2a7a4 · outbound

This paper cites Adversarial example defense: Ensembles of weak defenses are not strong.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Adversarial example defense: Ensembles of weak defenses are not strong

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:12.474729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 89b49520-d5dd-4335-a021-1749c038c79a · outbound

This paper cites An overview of text-independent speaker recognition: From features to supervectors.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples An overview of text-independent speaker recognition: From features to supervectors

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:12.292776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 63032a66-06dd-4042-83c1-6f570293346f · outbound

This paper cites Physgan: Generating physical-world-resilient adversarial examples for autonomous driving.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Physgan: Generating physical-world-resilient adversarial examples for autonomous driving

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:12.105303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b88d707-6e98-4d03-a6cf-24d43763ee87 · outbound

This paper cites Learning multiple layers of features from tiny images.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Learning multiple layers of features from tiny images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.952604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56a80651-a1d4-4e7b-856e-526d64f4ed7b · outbound

This paper cites Adversarial examples in the physical world.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Adversarial examples in the physical world

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.770267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 778fe0f7-c7be-458c-b43e-a9b67b272b52 · outbound

This paper cites The mnist database of handwritten digits.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples The mnist database of handwritten digits

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.597553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ea0ce0d-65d5-43ad-ba28-29c0a8d801c0 · outbound

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

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:06.242168Z

Source-reported events for the cited work

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Observation d83b942a-23e2-43cb-a19c-a6bb62ee76dc · outbound

This paper cites Distributed representa- tions of words and phrases and their compositionality.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Distributed representa- tions of words and phrases and their compositionality

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.425501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7bbb448-2d5b-494d-84b0-c500a4a9feab · outbound

This paper cites Universal adversarial perturbations.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Universal adversarial perturbations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.239613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0af56ed1-41c3-46bf-978d-c34ebfa1035c · outbound

This paper cites Librispeech: an asr corpus based on public domain audio books.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Librispeech: an asr corpus based on public domain audio books

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:06.562144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a332b05b-0681-4575-84ef-4d09a6a76820 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Distillation as a defense to adversarial perturbations against deep neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:11.065412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70b7546f-827b-45a0-a4d7-3d6d45f00c0a · outbound

This paper cites Deepxplore: Automated whitebox testing of deep learning systems.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Deepxplore: Automated whitebox testing of deep learning systems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:10.904678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c2f0d437-1757-46c8-9b0a-215fc11e5dc1 · outbound

This paper cites Foolbox: A Python toolbox to benchmark the robustness of machine learning models.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:06.861003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad991c1f-5ae5-4efe-af55-f50d54e74607 · outbound

This paper cites Deep learning in medical image analysis.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Deep learning in medical image analysis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:10.729261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 285f2424-e605-4d80-b18a-c0383ed78527 · outbound

This paper cites An abstract domain for certifying neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples An abstract domain for certifying neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:10.492861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6dfb06d-7b95-4cc3-b486-40a7df2d41d8 · outbound

This paper cites an unresolved cited work.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:47:10.162154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d49d0b2-9b10-48df-998f-ee8f31a36c7e · outbound

This paper cites Intriguing properties of neural networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Intriguing properties of neural networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:07.222769Z

Source-reported events for the cited work

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Observation 3f38775c-42b0-4b13-897d-9834346cfda6 · outbound

This paper cites Evaluating Robustness of Neural Networks with Mixed Integer Programming.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Evaluating Robustness of Neural Networks with Mixed Integer Programming

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T12:47:07.329494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34aa2291-47e9-4319-a2d4-23e5bd1ac0b4 · outbound

This paper cites Dissector: Input validation for deep learning applications by crossing-layer dissection.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Dissector: Input validation for deep learning applications by crossing-layer dissection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:09.862423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e706821-6d49-4fda-b6d3-e8ce90fe3597 · outbound

This paper cites Adversarial sample detection for deep neural network through model mutation testing.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Adversarial sample detection for deep neural network through model mutation testing

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:09.544049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c2770c1c-a5f6-4e11-9ccf-ce6106c2e0e6 · outbound

This paper cites Towards fast computation of certified robustness for relu networks.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Towards fast computation of certified robustness for relu networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:09.232633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 044fd525-9959-4665-8225-eff7fff5288d · outbound

This paper cites Evaluating the robustness of neural networks: An extreme value theory approach.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Evaluating the robustness of neural networks: An extreme value theory approach

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:08.996450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e34e3104-4d91-4c4c-a99e-743d96158051 · outbound

This paper cites Adversarial sample detection for speaker verification by neural vocoders.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Adversarial sample detection for speaker verification by neural vocoders

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:08.749347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f78ac0fb-6dcd-4b12-8713-1b2e9caf5fe1 · outbound

This paper cites Droid-sec: deep learning in android malware detection.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Droid-sec: deep learning in android malware detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:08.537365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 35fec0fa-50b2-4889-929e-95493d4a05d9 · outbound

This paper cites Attack as defense: Characterizing adversarial examples using robustness.

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples Attack as defense: Characterizing adversarial examples using robustness

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:47:08.365228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.