Paper Citation Record · LEDGER

ARMOR: Shielding Unlearnable Examples against Data Augmentation

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 3 inbound Pith citation observations for arXiv:2501.08862.

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

pith.paper-citation-record.v1
2501.08862 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:19:48.353611Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:46:59.087462Z

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

54 of 54 outbound references displayed

verified exact0
verified fuzzy46
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 92b17d5d-b8cc-46af-9bab-920eb86c7492 · outbound

This paper cites Privacy-preserving machine learning: Threats and solutions.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Privacy-preserving machine learning: Threats and solutions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.162908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f3272a2-f2b4-4786-91b5-68d7820efd7e · outbound

This paper cites Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.149242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fee88e4f-3093-400e-85ae-5a117add62dd · outbound

This paper cites Membership inference attacks from first principles.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Membership inference attacks from first principles

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.134370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 37c2f71c-cdbf-4d84-b8b7-f056e58ca26a · outbound

This paper cites A group-theoretic framework for data augmentation.

ARMOR: Shielding Unlearnable Examples against Data Augmentation A group-theoretic framework for data augmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.120474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c7ffd01-05cc-40a1-9c55-072718a736f6 · outbound

This paper cites LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.

ARMOR: Shielding Unlearnable Examples against Data Augmentation LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.125387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89bc0cb1-cce0-4dd1-9bee-cf33547b1d8d · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Autoaugment: Learning augmentation strategies from data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.106206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5e5b051-83f3-4a3f-88f8-631ac6bce115 · outbound

This paper cites Ran- daugment: Practical automated data augmentation with a reduced search space.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Ran- daugment: Practical automated data augmentation with a reduced search space

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.091948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b1e5804-0ab5-4e9d-91e9-d9fcdcd2a95b · outbound

This paper cites Imagenette: A smaller subset of 10 easily classified classes from imagenet.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Imagenette: A smaller subset of 10 easily classified classes from imagenet

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.078275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79e9ef43-6270-4a9e-b38f-08e5c8625da8 · outbound

This paper cites Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.143474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24f2ff96-b04f-451b-8803-ebc69c6af769 · outbound

This paper cites Adversarial examples make strong poisons.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Adversarial examples make strong poisons

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.064199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99f9aeb3-65ef-4451-9ebe-bf6a09610074 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Model inversion attacks that exploit confidence information and basic countermeasures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.049024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 47ac0ed5-fe6d-45f6-ab90-fd9a73c9c9cf · outbound

This paper cites Ro- bust unlearnable examples: Protecting data against adversarial learning.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Ro- bust unlearnable examples: Protecting data against adversarial learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.034941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f73934e1-e09f-46de-a92f-2dd68914c85c · outbound

This paper cites An introduction to the california consumer privacy act (ccpa).

ARMOR: Shielding Unlearnable Examples against Data Augmentation An introduction to the california consumer privacy act (ccpa)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.019822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60ac2bd2-0b2f-4e17-b043-26ca0a38f69d · outbound

This paper cites Attribute inference attacks in online social networks.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Attribute inference attacks in online social networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:49.004231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8adcb95-26d9-4ff8-9e39-9a2d6ecd9c44 · outbound

This paper cites Private data inference attacks against cloud: Model, technologies, and research directions.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Private data inference attacks against cloud: Model, technologies, and research directions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.989722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation efea2aac-a1cc-4eeb-bf6e-82b18c53c016 · outbound

This paper cites Netguard: Protecting commercial web apis from model inversion attacks using gan-generated fake samples.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Netguard: Protecting commercial web apis from model inversion attacks using gan-generated fake samples

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.974513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81292e3a-fa8c-4743-9129-daadbdf5319e · outbound

This paper cites Faster autoaugment: Learning augmentation strategies using backprop- agation.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Faster autoaugment: Learning augmentation strategies using backprop- agation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.959539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 69f702fb-0f54-4c26-ad05-8174b1441308 · outbound

This paper cites Protecting facial privacy: generating ad- versarial identity masks via style-robust makeup transfer.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Protecting facial privacy: generating ad- versarial identity masks via style-robust makeup transfer

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.943691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc8b24d6-7c75-45f1-afd1-67eca6b75ffe · outbound

This paper cites Unlearnable examples: Making personal data un- exploitable.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Unlearnable examples: Making personal data un- exploitable

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.928956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 694dbff5-b467-40be-af7a-533e038e1d46 · outbound

This paper cites Fast adversarial training with adaptive step size, 2022.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Fast adversarial training with adaptive step size, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.913461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b2007f30-265f-4875-87c5-a83078efeed3 · outbound

This paper cites Puzzle mix: Ex- ploiting saliency and local statistics for optimal mixup.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Puzzle mix: Ex- ploiting saliency and local statistics for optimal mixup

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.898129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10705842-d156-4d44-931d-b52874b0ca80 · outbound

This paper cites Swift: Super-fast and robust privacy-preserving machine learning.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Swift: Super-fast and robust privacy-preserving machine learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.882329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ef8ad1d-9e14-49d1-92d1-9ad43eba6d9b · outbound

This paper cites Learning multiple layers of features from tiny images.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Learning multiple layers of features from tiny images

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.205164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61edf612-6918-4256-935b-175a1e2bd3a4 · outbound

This paper cites Adversarial machine learning at scale.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Adversarial machine learning at scale

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.858109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd3141e7-2322-4466-b92c-1c677aa353e8 · outbound

This paper cites Fast autoaugment.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Fast autoaugment

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.841536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80974c03-5e05-4ea4-9790-f803b373f03f · outbound

This paper cites Going Grayscale: The Road to Understanding and Improving Unlearnable Examples.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.218407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3008ff65-dee5-417b-9452-4de890230846 · outbound

This paper cites Feature distillation: DNN-oriented JPEG compression against adversarial examples.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Feature distillation: DNN-oriented JPEG compression against adversarial examples

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.827281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9aeb6e22-5854-4bb9-9131-65a6518ac0df · outbound

This paper cites Discriminator-free generative adversarial attack.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Discriminator-free generative adversarial attack

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.813164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bdafffa-068f-472a-8f10-2a6c3616da17 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Towards deep learning models resistant to adversarial attacks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.799997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 65e0b70e-a8d6-47be-93a8-1993e0f6da35 · outbound

This paper cites A review: Data pre-processing and data augmentation techniques.

ARMOR: Shielding Unlearnable Examples against Data Augmentation A review: Data pre-processing and data augmentation techniques

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.785783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5ec9f769-7d6b-4a2a-89b1-27fd07d03638 · outbound

This paper cites Deep learning applications and challenges in big data analytics.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Deep learning applications and challenges in big data analytics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.772399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49b2aff9-e5f7-4991-89b4-3cc275255d5f · outbound

This paper cites Deep face recognition.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Deep face recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.758214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0024e676-2c3d-49f5-bbaa-25c483948691 · outbound

This paper cites Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.743735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4d1d38c-37ea-4ce4-b686-aca0af236604 · outbound

This paper cites FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques.

ARMOR: Shielding Unlearnable Examples against Data Augmentation FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.256789Z

Source-reported events for the cited work

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Observation c30a777e-c56c-40dc-8c08-0dc10bcd7306 · outbound

This paper cites Data Poisoning Won't Save You From Facial Recognition.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Data Poisoning Won't Save You From Facial Recognition

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.263072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c59ca6-dc92-43a4-ae4a-ab840344889e · outbound

This paper cites Data augmentation can improve robustness.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Data augmentation can improve robustness

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.729543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33bcdbd0-be7e-4456-bfa2-734ea695b008 · outbound

This paper cites Transferable unlearnable examples, 2022.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Transferable unlearnable examples, 2022

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.715539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70878829-5d5e-4243-9ad1-7c682996e431 · outbound

This paper cites Fawkes: Protecting privacy against unauthorized deep learning models.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Fawkes: Protecting privacy against unauthorized deep learning models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.701767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b38396c3-4006-4132-81bb-b53dba975bef · outbound

This paper cites Membership inference attacks against machine learning models.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Membership inference attacks against machine learning models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.687693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6a73052-4a6c-4c57-92dc-85ce5e0359f0 · outbound

This paper cites Smith and Nicholay Topin.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Smith and Nicholay Topin

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.287598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0db3930-2a2a-4a77-ab33-2ef68ea87a33 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Revisiting unreasonable effectiveness of data in deep learning era

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.663273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aaf612c3-b295-4580-adf6-9335d10f0166 · outbound

This paper cites Mitigating membership inference attacks by self-distillation through a novel ensemble architec- ture.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Mitigating membership inference attacks by self-distillation through a novel ensemble architec- ture

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.648994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfa0012f-dba6-487e-9801-55c93450156a · outbound

This paper cites The calculation of posterior distributions by data augmentation.

ARMOR: Shielding Unlearnable Examples against Data Augmentation The calculation of posterior distributions by data augmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.635933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c7110f71-6f37-4737-a155-252909b9e3e4 · outbound

This paper cites The art of data augmentation.

ARMOR: Shielding Unlearnable Examples against Data Augmentation The art of data augmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.622812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ccf7dac2-9cfe-4f6c-8a02-121746f95376 · outbound

This paper cites The eu general data protection regulation (gdpr).

ARMOR: Shielding Unlearnable Examples against Data Augmentation The eu general data protection regulation (gdpr)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.609018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99a190fe-947e-4398-9af2-0cfebd240958 · outbound

This paper cites The JPEG still picture compression standard.

ARMOR: Shielding Unlearnable Examples against Data Augmentation The JPEG still picture compression standard

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.595555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b70d6ce-faaf-4b5c-9c49-868f1b1b57d3 · outbound

This paper cites Non- local neural networks.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Non- local neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.579957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5a8282f-3d24-409c-b9c2-90b5a9bb6750 · outbound

This paper cites Recent advances in deep learning.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Recent advances in deep learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.563549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 340625db-e9fd-44e2-b755-e43a310c2eb8 · outbound

This paper cites Patchguard: A provably robust defense against adversarial patches via small receptive fields and masking.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Patchguard: A provably robust defense against adversarial patches via small receptive fields and masking

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.546958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80b8ba37-6eca-4a37-951f-a8a26ea4837c · outbound

This paper cites Neural tangent generalization attacks.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Neural tangent generalization attacks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.532425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd9325d7-3b4d-4287-a410-d25f243ac8a6 · outbound

This paper cites A data augmentation-based defense method against adversarial attacks in neural networks.

ARMOR: Shielding Unlearnable Examples against Data Augmentation A data augmentation-based defense method against adversarial attacks in neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.517639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfd4e891-087d-4362-8668-8b58b453fa24 · outbound

This paper cites You only propagate once: Accelerating adversarial training via maximal principle, 2019.

ARMOR: Shielding Unlearnable Examples against Data Augmentation You only propagate once: Accelerating adversarial training via maximal principle, 2019

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.501314Z

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Observation ec069a34-88ca-480c-8128-86d95a2ace35 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

ARMOR: Shielding Unlearnable Examples against Data Augmentation mixup: Beyond Empirical Risk Minimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T20:19:48.348732Z

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Observation 330cdf9c-4188-4657-924f-06fb478acc03 · outbound

This paper cites Top Minds.

ARMOR: Shielding Unlearnable Examples against Data Augmentation Top Minds

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:19:48.484588Z

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Pith citing papers

Observation a2f30960-d311-457e-97e2-8dcd1286bc8a · inbound

T2UE: Generating Unlearnable Examples from Text Descriptions cites this paper.

T2UE: Generating Unlearnable Examples from Text Descriptions ARMOR: Shielding Unlearnable Examples against Data Augmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T04:46:59.087462Z

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Observation a9081433-8a96-4941-9378-6912f1c958c8 · inbound

SoK: Unlearnability and Unlearning for Model Dememorization cites this paper.

SoK: Unlearnability and Unlearning for Model Dememorization ARMOR: Shielding Unlearnable Examples against Data Augmentation

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:47:04.283258Z

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Observation 7b81f646-7865-4ed5-a66f-1a0cdbfae475 · inbound

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions cites this paper.

SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions ARMOR: Shielding Unlearnable Examples against Data Augmentation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:56.162356Z

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