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Paper Citation Record · LEDGER

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2412.05943.

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

pith.paper-citation-record.v1
2412.05943 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:15:05.811377Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy16
  • unresolved15
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation ccbc1e16-94d3-4763-ae43-f3399d9ee73c · outbound

This paper cites Biggio, I.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Biggio, I

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7be3103d-2347-4bc9-85d4-6f0b7c704a93 · outbound

This paper cites Chapelle, B.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Chapelle, B

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 54c11069-0c01-4d0d-957f-1365584cf44c · outbound

This paper cites Dabov, A.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Dabov, A

Reference 3

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Unavailable: canonical work link unavailable.

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Observation bec0e903-157b-4d57-bb16-81290d55642c · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7a8c49e6-2d10-4e78-b672-2e0e5c2d3634 · outbound

This paper cites F awzi, H.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling F awzi, H

Reference 5

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Observation 69934d6f-84f3-4275-a4a7-dbf2efa14891 · outbound

This paper cites Gilmer, N.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Gilmer, N

Reference 6

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ba1cb3a4-87ff-4fa9-86dc-17c40c316b34 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Explaining and Harnessing Adversarial Examples

Reference 7

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Observation 21cf7ac6-3280-4113-8e8c-0b0b07eddd8e · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c0a8271d-b8e5-43c6-a55f-8f782477d643 · outbound

This paper cites Adversarial Machine Learning at Scale.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Adversarial Machine Learning at Scale

Reference 9

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source=pdf_text observed=2026-08-11T20:15:05.739116Z digest=sha256:87ffcf98e5ba837fc7f5db5ea303c9e406da985df42c4bd16281859adc5aca04

Observation a010fec1-b593-4f47-8f32-486ed8017841 · outbound

This paper cites Liang, J.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Liang, J

Reference 10

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Observation 4634f378-7425-46f2-bc2f-eb1cc389333e · outbound

This paper cites Lysaker, A.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Lysaker, A

Reference 11

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7f3776ef-32dc-4069-9cbb-cf3a7bc9ef2c · outbound

This paper cites Mahloujifar, D.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Mahloujifar, D

Reference 12

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 32126498-2721-4a36-afc6-8c8cacec522d · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 13

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 22811679-14a0-4881-8878-e547a2ec91aa · outbound

This paper cites Mkadry, A.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Mkadry, A

Reference 14

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 1b9fd722-ad62-4569-9201-6440f9bdf454 · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 15

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T20:15:05.757329Z digest=sha256:d2e7335af161928121da1ba3ceac8a0c28516ae40598058fb39a6e47b34b8d27

Observation 3c04a673-fc43-45e4-a31d-765651f49c1e · outbound

This paper cites Nguyen, J.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Nguyen, J

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T20:15:05.760497Z digest=sha256:f24743023007c780f897e3b2e3bb7f0e2ccd37b927b7f78e60e8a5be3cd0abac

Observation 2a121848-25e5-4ae8-b9d3-c2ec04545d43 · outbound

This paper cites Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack

Reference 17

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 758f96d6-7017-432f-b288-e0964b5dc956 · outbound

This paper cites Perona and J.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Perona and J

Reference 18

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Observation 36b3d000-c580-4a5f-8a64-524eb397b9b2 · outbound

This paper cites Rajwade, A.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Rajwade, A

Reference 19

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Observation e013a698-3a57-41fc-994f-b14e8a771175 · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 20

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation c7d8e1fd-8185-46df-b14b-6a12eef4b42a · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 21

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Observation 25c5fc81-96ba-4859-b670-5a5f243556bd · outbound

This paper cites Salamat, M.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Salamat, M

Reference 22

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Observation 505e5967-6d21-4030-9e21-f0c549451783 · outbound

This paper cites Are adversarial examples inevitable?.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Are adversarial examples inevitable?

Reference 23

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Observation ab17d911-8b72-4e29-8eb6-c5efb517c64d · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 24

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation b5ab05ee-c19f-42a4-bf59-eff9045a2558 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Intriguing properties of neural networks

Reference 25

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source=pdf_text observed=2026-08-11T20:15:05.788776Z digest=sha256:a450f6a7e51c56efcc69e16edaab73aa02a037bf67cb1eb0e6d25cd789ccf50e

Observation 728fe460-41bb-4bf3-a1ce-28cd20092c71 · outbound

This paper cites Thomas and A.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Thomas and A

Reference 26

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ad8d16aa-f9b2-4a53-bb32-f0af78a8cde0 · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 03d4f38c-a113-4926-a483-6dd06895346e · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 0a0bf813-6939-45ca-9a17-27f320e8b82e · outbound

This paper cites an unresolved cited work.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Unresolved cited work

Reference 29

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 0d956d2a-9cbd-46e2-ad45-46c6937d43ae · outbound

This paper cites Zhang, Y.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Zhang, Y

Reference 30

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raw_fallback, observed 2026-08-11T20:15:06.061923Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T20:15:05.802676Z digest=sha256:10901188b64751aeeabf7b968624390b36b4f44b22d3965f7c485276d133206f

Observation 43716b75-3241-441e-8fb0-aba66316a2be · outbound

This paper cites Zhang, W.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Zhang, W

Reference 31

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raw_fallback, observed 2026-08-11T20:15:06.053274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T20:15:05.805270Z digest=sha256:c6f66987fd85075b1801093b39a85c096394d0c3d33e38b535934bc4f3c0609d

Observation 457b3588-c154-421f-85a6-f3c05d1f476a · outbound

This paper cites Zhang, J.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Zhang, J

Reference 32

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raw_fallback, observed 2026-08-11T20:15:06.044437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 555dafcc-679b-4a00-afa6-3ea0b59d3e60 · outbound

This paper cites Zhou and S.

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling Zhou and S

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers

No inbound Pith citation observations are available.