Paper Citation Record · LEDGER

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.12613.

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

pith.paper-citation-record.v1
2506.12613 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:54:02.492560Z

measured 28 of 28 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

28 of 28 outbound references displayed

verified exact0
verified fuzzy21
unresolved7
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malformed identifier0
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External citation measurements

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Outbound references

Observation 3551b750-292f-4869-822a-433ff27b0b11 · outbound

This paper cites Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:00.501704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 669ce2f3-c089-4662-8c29-e1ab20cd3dcb · outbound

This paper cites Adversarial examples in multi-layer random relu networks.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial examples in multi-layer random relu networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.839168Z

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

Observation c49ec9fa-6f88-4946-b01d-f767b365690a · outbound

This paper cites Adversarial examples from compu- tational constraints.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial examples from compu- tational constraints

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.691176Z

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

Observation ffa0e123-261c-4fc1-97d9-1f0c7b565584 · outbound

This paper cites A single gradient step finds adversarial examples on random two-layers neural networks.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ A single gradient step finds adversarial examples on random two-layers neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.518722Z

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 5112a76b-9312-4ecd-b0a8-da76b7130bcd · outbound

This paper cites Adversarial examples are not easily detected: Bypassing ten detection methods.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial examples are not easily detected: Bypassing ten detection methods

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.375807Z

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 6e82c6e5-70e5-4fbe-848c-2132927fa029 · outbound

This paper cites Audio adversarial examples: Targeted attacks on speech-to-text.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Audio adversarial examples: Targeted attacks on speech-to-text

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.254444Z

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 3caf20ed-c91d-4fe5-b9a1-4962e9274420 · outbound

This paper cites Most relu networks suffer from ℓ2 adversarial perturbations.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Most relu networks suffer from ℓ2 adversarial perturbations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:05.135338Z

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

Observation 9924e4fe-b7e8-478e-9b0b-d5773feda6e8 · outbound

This paper cites Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.994006Z

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 e0cc0588-1f5b-4b39-b46f-d36d3f6a4667 · outbound

This paper cites Adversarial vulnerability for any classifier.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial vulnerability for any classifier

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.799386Z

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 fde21171-71e5-41c6-b563-a5122d4dfd15 · outbound

This paper cites Detecting Adversarial Samples from Artifacts.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Detecting Adversarial Samples from Artifacts

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:01.161725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d1f8d8e-4303-4282-b911-04ed601196a1 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Understanding the difficulty of training deep feedforward neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.619150Z

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

Observation 1381d2ff-7b24-4573-864e-e1836026f8e5 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Explaining and Harnessing Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:01.323557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aec7052-d659-48f2-b6c1-41b000ec55eb · outbound

This paper cites On the (Statistical) Detection of Adversarial Examples.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ On the (Statistical) Detection of Adversarial Examples

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:01.379464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14db1abb-1382-4b22-a830-0ae8b071d0ca · outbound

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

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:01.444353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0184b3b-a458-4b0d-b7b8-a03255577f65 · outbound

This paper cites The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.479525Z

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 34204e18-5e29-4a96-8f80-636514c48be8 · outbound

This paper cites The random matrix theory of the classical compact groups , volume 218.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ The random matrix theory of the classical compact groups , volume 218

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.310566Z

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 ad38878d-50dc-4615-880d-a107e44e8384 · outbound

This paper cites Adversarial examples exist in two-layer relu networks for low dimensional linear subspaces.Advances in Neural Information Processing Systems, 36:5028–5049,.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial examples exist in two-layer relu networks for low dimensional linear subspaces.Advances in Neural Information Processing Systems, 36:5028–5049,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:04.147026Z

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 044d1ad0-ac5f-486d-9959-45a45bc3ca1b · outbound

This paper cites Adversarial examples in random neural networks with general activations.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversarial examples in random neural networks with general activations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.977768Z

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 862caa1c-aafa-4bb0-99e4-36eaf160ab6b · outbound

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

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Distillation as a defense to adversarial perturbations against deep neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.803613Z

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 1a29aecc-cdbc-410c-b016-86d6673bdcc6 · outbound

This paper cites Practical black-box attacks against machine learning.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Practical black-box attacks against machine learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.658837Z

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 2cfff707-d606-4199-9cc9-36e12d1e3cae · outbound

This paper cites Adversar- ially robust generalization requires more data.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Adversar- ially robust generalization requires more data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.518597Z

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 f979a8ac-bc09-43e4-9328-c4e5ca8a730c · outbound

This paper cites Are adversarial examples inevitable?.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Are adversarial examples inevitable?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:01.996451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e52064a-fe0b-4d4a-a9a7-1b0397f2e8a9 · outbound

This paper cites A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:02.071049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 352ab5f9-7f38-434e-b1be-0db22247dc10 · outbound

This paper cites Intriguing properties of neural networks.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Intriguing properties of neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.370700Z

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

Observation e875554e-e0d7-42bd-a2d7-6ddd933405e7 · outbound

This paper cites Probability in high dimension.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Probability in high dimension

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.219628Z

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 1b4fe9ea-e443-40f1-9233-58a15a95dfa8 · outbound

This paper cites Gradient methods provably converge to non-robust networks.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Gradient methods provably converge to non-robust networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:03.082707Z

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 4348eb0f-94be-47cf-9191-62ff6280edb3 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science , volume 47.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ High-dimensional probability: An introduction with applications in data science , volume 47

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:02.911139Z

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 f68c7ce8-8075-449a-b459-0f2ce9064cac · outbound

This paper cites Provable defenses against adversarial examples via the convex outer adver- sarial polytope.

Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{so}(d)$ Provable defenses against adversarial examples via the convex outer adver- sarial polytope

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:54:02.738333Z

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

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