Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.17626.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:32:37.251990Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T22:46:53.157620Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5bbcb557-eef1-47ff-a11b-7044d4c2c8ee · inbound
Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting cites this paper.
Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T13:13:09.370816Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd228681-debf-4fbd-884c-44a6458b6f6d · inbound
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models cites this paper.
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:35:35.411237Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d282564d-52f2-49cf-b4e6-b22d296e4a16 · inbound
DUMB and DUMBer: Is Adversarial Training Worth It in the Real World? cites this paper.
DUMB and DUMBer: Is Adversarial Training Worth It in the Real World? A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:20:07.043690Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ba4dca7d-03d2-4f73-9672-5db97a78d327 · inbound
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation cites this paper.
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:58:50.446639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56222ccc-bad0-41b3-bc0a-3c1f4724716f · inbound
Understanding Knowledge Transferability for Transfer Learning: A Survey cites this paper.
Understanding Knowledge Transferability for Transfer Learning: A Survey A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:22:24.934363Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37d80c18-f0d2-4e90-8100-6ddb8e25a668 · inbound
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning cites this paper.
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:30.747065Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a847a75-a77f-4d5b-85f3-b0c69ec1329f · inbound
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:46:53.160375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b87a6198-a019-49f0-a735-edfad83a569b · inbound
I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks cites this paper.
I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:10:08.244035Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd7a417b-30fb-445f-995b-ec04bd89482b · inbound
Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks cites this paper.
Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:32:37.251990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.