Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:29.820285Z
Paper Citation Record · LEDGER
Are classical deep neural networks weakly adversarially robust?
As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.02016.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact0
- verified fuzzy9
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2014eeb4-9857-43c4-ab57-fe167d140803 · outbound
This paper cites Intriguing properties of neural networks.Computer Science, 2013.
Are classical deep neural networks weakly adversarially robust? Intriguing properties of neural networks.Computer Science, 2013
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:31.166120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ac5ab3d2-783e-430d-9a3e-8d605eb61f9c · outbound
This paper cites Univer- sal adversarial perturbations.
Are classical deep neural networks weakly adversarially robust? Univer- sal adversarial perturbations
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.689593Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aeeed728-7855-44e2-89b6-8acb571d6151 · outbound
This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.
Are classical deep neural networks weakly adversarially robust? Goodfellow, Jonathon Shlens, and Christian Szegedy
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:31.041909Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ea67c754-cc8b-4734-bb88-873643d07d82 · outbound
This paper cites Ensemble Adversarial Training: Attacks and Defenses.
Are classical deep neural networks weakly adversarially robust? Ensemble Adversarial Training: Attacks and Defenses
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.922969Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d76a82c5-75c2-4a00-b5b9-af1c7d913e9b · outbound
This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.
Are classical deep neural networks weakly adversarially robust? Towards Deep Learning Models Resistant to Adversarial Attacks
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.017286Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3598dc6b-8e97-4531-abbe-345ccb2d06b0 · outbound
This paper cites Towards evaluating the robustness of neural networks.
Are classical deep neural networks weakly adversarially robust? Towards evaluating the robustness of neural networks
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.076882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74f84cc6-8289-43e1-a5fc-ec6cff4a4e25 · outbound
This paper cites Distillation as a defense to adversarial perturbations against deep neural networks.
Are classical deep neural networks weakly adversarially robust? Distillation as a defense to adversarial perturbations against deep neural networks
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.908959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation baf6b9f1-8d8c-440c-b611-db526453d148 · outbound
This paper cites Deepfool: a simple and accurate method to fool deep neural networks.
Are classical deep neural networks weakly adversarially robust? Deepfool: a simple and accurate method to fool deep neural networks
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.291023Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 598088fc-9c10-491d-a3b9-2b377d4fe6de · outbound
This paper cites Adversarial examples are not bugs, they are features.
Are classical deep neural networks weakly adversarially robust? Adversarial examples are not bugs, they are features
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.742916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 609347f9-949e-4493-9e78-6d9a1eaafc8e · outbound
This paper cites Adversarial sample detection through neural network transport dynamics.
Are classical deep neural networks weakly adversarially robust? Adversarial sample detection through neural network transport dynamics
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.588139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e53f8521-c75b-4a20-b999-47d4d604c670 · outbound
This paper cites Progressive Feedforward Collapse of ResNet Training.
Are classical deep neural networks weakly adversarially robust? Progressive Feedforward Collapse of ResNet Training
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.521845Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e52c5d04-7885-43c5-9c25-431680726646 · outbound
This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 2020.
Are classical deep neural networks weakly adversarially robust? Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 2020
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.380635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e35dd667-9f32-4250-b8e2-a8572b450d95 · outbound
This paper cites A law of data separation in deep learning.Proceedings of the National Academy of Sciences, 120(36):e2221704120, 2023.
Are classical deep neural networks weakly adversarially robust? A law of data separation in deep learning.Proceedings of the National Academy of Sciences, 120(36):e2221704120, 2023
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.290808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 81a99a67-1d96-4df7-ae12-11085efd5fb0 · outbound
This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.
Are classical deep neural networks weakly adversarially robust? Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:30.175116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4df51198-b8b6-4119-89e0-4bc182444f25 · outbound
This paper cites A threshold selection method from gray-level histograms.IEEE Transactions on Systems Man & Cybernetics, 9(1):62–66, 2007.
Are classical deep neural networks weakly adversarially robust? A threshold selection method from gray-level histograms.IEEE Transactions on Systems Man & Cybernetics, 9(1):62–66, 2007
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:29.992401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.