Paper Citation Record · LEDGER

Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.00753.

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

pith.paper-citation-record.v1
2007.00753 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:54:28.533204Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

28
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4158fb71-3318-408e-8a41-24726b59f4e6 · inbound

Approach to Finding a Robust Deep Learning Model cites this paper.

Approach to Finding a Robust Deep Learning Model Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:54:28.533204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a3a510-f11c-409c-ad17-00ee8da2c1ff · inbound

Certifiably robust malware detectors by design cites this paper.

Certifiably robust malware detectors by design Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T22:21:08.176845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0c9225a-6a6b-4ee2-9761-d7f8986c44c3 · inbound

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control cites this paper.

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:32:49.445446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f75f80a8-ffa6-4fa8-8a45-2a5ad9e231ae · inbound

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing cites this paper.

Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:39:48.102540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90b1b9da-c1da-40fe-9589-6fabc45fe651 · inbound

Detecting Adversarial Data via Provable Adversarial Noise Amplification cites this paper.

Detecting Adversarial Data via Provable Adversarial Noise Amplification Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:26:06.102783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.