Paper Citation Record · LEDGER

Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2303.15127.

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

pith.paper-citation-record.v1
2303.15127 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:34:49.733556Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T04:24:32.497204Z

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 9f358a01-80e7-43fc-ae3c-59a0cbbd6b9b · inbound

Nonlinear Transformations Against Unlearnable Datasets cites this paper.

Nonlinear Transformations Against Unlearnable Datasets Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-25T09:10:33.816571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f3535c41-bcf2-429f-bd06-7c8674a2a412 · inbound

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training cites this paper.

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:46:34.349550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3cb843e0-0d66-425e-8633-f302a4ef0986 · inbound

Dual-branch Robust Unlearnable Examples cites this paper.

Dual-branch Robust Unlearnable Examples Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:50:59.352479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c45869fe-5846-47be-9611-ab78e36592c3 · inbound

Dual-branch Robust Unlearnable Examples cites this paper.

Dual-branch Robust Unlearnable Examples Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T00:45:12.301851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e0aea3f-47aa-4a7f-a25e-5da07434fca1 · inbound

Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms cites this paper.

Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T07:11:53.505897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44ee9afa-e816-4760-bc16-353387ec35a1 · inbound

Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles cites this paper.

Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T04:24:32.499121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 35d499f9-cdb4-457f-a034-de32e149c13a · inbound

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders cites this paper.

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T10:41:43.164852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b907b1ea-677a-40f7-89c4-d72fc6ed1405 · inbound

Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era cites this paper.

Reversible Unlearnable Examples: Towards the Copyright Protection in Deep Learning Era Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:49.733556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.