Paper Citation Record · LEDGER

Random Sampling for Diffusion-based Adversarial Purification

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2411.18956.

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

pith.paper-citation-record.v1
2411.18956 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:47:24.538466Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

39 of 39 outbound references displayed

verified exact1
verified fuzzy21
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 362e855b-a4c0-490e-ac0a-e49940645495 · outbound

This paper cites Deep learning with differential privacy.

Random Sampling for Diffusion-based Adversarial Purification Deep learning with differential privacy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.405292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 660f7639-1e1b-443a-98e3-2b0b55d12d61 · outbound

This paper cites GPT-4 Technical Report.

Random Sampling for Diffusion-based Adversarial Purification GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.409827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcd862ca-c124-491d-b6f6-9379458f3ea2 · outbound

This paper cites Synthesizing robust adversarial examples.

Random Sampling for Diffusion-based Adversarial Purification Synthesizing robust adversarial examples

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.929675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 92220e3c-1095-44c3-9167-89c6ae7abb41 · outbound

This paper cites Security and Privacy Issues in Deep Learning.

Random Sampling for Diffusion-based Adversarial Purification Security and Privacy Issues in Deep Learning

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-12T10:47:24.642796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b905cccb-ea7c-4a51-bd20-ab3103c93524 · outbound

This paper cites A survey of deep learning methods for cyber security.

Random Sampling for Diffusion-based Adversarial Purification A survey of deep learning methods for cyber security

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.919457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation de55d680-c034-42b3-a721-60556541df31 · outbound

This paper cites Diffusion pos- terior sampling for general noisy inverse problems.

Random Sampling for Diffusion-based Adversarial Purification Diffusion pos- terior sampling for general noisy inverse problems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.907905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af841c6c-6473-4c73-b1ca-7f7264bf29bd · outbound

This paper cites Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks.

Random Sampling for Diffusion-based Adversarial Purification Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.896766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a5025691-4ebd-4363-a275-3391ea791be4 · outbound

This paper cites Robustbench: a standardized adversarial robustness benchmark.

Random Sampling for Diffusion-based Adversarial Purification Robustbench: a standardized adversarial robustness benchmark

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.886431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef1b38d5-d091-477d-a926-469c089f6b6e · outbound

This paper cites Re- sisting adversarial attacks using gaussian mixture variational autoencoders.

Random Sampling for Diffusion-based Adversarial Purification Re- sisting adversarial attacks using gaussian mixture variational autoencoders

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.876197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 268eeae5-8ce4-4a4b-a4aa-0016b5156023 · outbound

This paper cites Improv- ing robustness using generated data.

Random Sampling for Diffusion-based Adversarial Purification Improv- ing robustness using generated data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.865571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 779dc4c1-9a11-409c-ab1d-3f7e56e63e9e · outbound

This paper cites Deep residual learning for image recognition.

Random Sampling for Diffusion-based Adversarial Purification Deep residual learning for image recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.442095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df67d41-a468-45f3-a666-95e86c4e0310 · outbound

This paper cites Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models.

Random Sampling for Diffusion-based Adversarial Purification Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.446229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bfd6e2b-0484-4a3c-b877-aae532c364fa · outbound

This paper cites Stochastic security: Adversarial defense using long-run dy- namics of energy-based models.

Random Sampling for Diffusion-based Adversarial Purification Stochastic security: Adversarial defense using long-run dy- namics of energy-based models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.848117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e368f021-5a1c-41eb-91d1-7830a847b0f3 · outbound

This paper cites DISCO: Adversarial defense with local implicit functions.

Random Sampling for Diffusion-based Adversarial Purification DISCO: Adversarial defense with local implicit functions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.838013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ebb72706-b706-4340-b1b6-a5bf00b4b8cd · outbound

This paper cites Denoising dif- fusion probabilistic models.

Random Sampling for Diffusion-based Adversarial Purification Denoising dif- fusion probabilistic models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.457318Z

Source-reported events for the cited work

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Observation 1e885606-d34b-4de2-9ec1-06370e577b94 · outbound

This paper cites Sta- ble neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks.

Random Sampling for Diffusion-based Adversarial Purification Sta- ble neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.822743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95e6c587-35b8-4179-8c6e-3dfab051209e · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Random Sampling for Diffusion-based Adversarial Purification Imagenet classification with deep convolutional neural net- works

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.464021Z

Source-reported events for the cited work

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Observation 3fb15414-f297-4adb-8078-0ad45ce26710 · outbound

This paper cites Robust evaluation of diffusion-based adversarial purification, 2023.

Random Sampling for Diffusion-based Adversarial Purification Robust evaluation of diffusion-based adversarial purification, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.805097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c9c6c89a-5f52-4f13-bafc-0b74c671e34a · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Random Sampling for Diffusion-based Adversarial Purification Swin transformer: Hierarchical vision transformer using shifted windows

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.470422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ae3eea0-418a-4c47-9568-31fb136d91fb · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Random Sampling for Diffusion-based Adversarial Purification Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.473849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 806c6ff6-8044-4992-b809-1ef72da752ae · outbound

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

Random Sampling for Diffusion-based Adversarial Purification Towards deep learning models resistant to adversarial attacks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.788703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 119b42ad-7483-4d89-b9bc-42a28b77a08d · outbound

This paper cites Learning in Implicit Generative Models.

Random Sampling for Diffusion-based Adversarial Purification Learning in Implicit Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.481149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7e0b7d3-3696-44a6-896c-674e5072c5c5 · outbound

This paper cites Diffusion Models for Adversarial Purification.

Random Sampling for Diffusion-based Adversarial Purification Diffusion Models for Adversarial Purification

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.485280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c177e66-defc-400e-87b5-137b53966a85 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Random Sampling for Diffusion-based Adversarial Purification Photorealistic text-to-image diffusion models with deep language understanding

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.489051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99bf57e6-b85e-402e-a8f5-80ab7b56f4c5 · outbound

This paper cites Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.

Random Sampling for Diffusion-based Adversarial Purification Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.492591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce2c7fd-0a6d-43ca-8707-60414b2ad302 · outbound

This paper cites Denois- ing diffusion implicit models.

Random Sampling for Diffusion-based Adversarial Purification Denois- ing diffusion implicit models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.770967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef5778f0-bdf6-4f3d-86f3-103ae683c3a0 · outbound

This paper cites Mimicd- iffusion: Purifying adversarial perturbation via mimicking clean diffusion model, 2023.

Random Sampling for Diffusion-based Adversarial Purification Mimicd- iffusion: Purifying adversarial perturbation via mimicking clean diffusion model, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.760982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e2c182e3-186b-4a17-b17d-2019437e068f · outbound

This paper cites Pixeldefend: Leveraging genera- tive models to understand and defend against adversarial ex- 9 amples.

Random Sampling for Diffusion-based Adversarial Purification Pixeldefend: Leveraging genera- tive models to understand and defend against adversarial ex- 9 amples

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.750120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0be5b1b-ad3b-4c71-91af-a58cdee88e7c · outbound

This paper cites Score-based generative modeling through stochastic differential equa- tions.

Random Sampling for Diffusion-based Adversarial Purification Score-based generative modeling through stochastic differential equa- tions

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.739296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0497a455-3168-4b2c-a55a-e6f8720d55df · outbound

This paper cites Robustifying models against adversarial attacks by langevin dynamics.

Random Sampling for Diffusion-based Adversarial Purification Robustifying models against adversarial attacks by langevin dynamics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.729364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1d3f5d7-93b7-4747-adf8-e6828233d692 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

Random Sampling for Diffusion-based Adversarial Purification Guided Diffusion Model for Adversarial Purification

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.512299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e384af05-ec05-46d8-bc91-13eb7fb34fcd · outbound

This paper cites Deep face recognition: A survey.

Random Sampling for Diffusion-based Adversarial Purification Deep face recognition: A survey

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.718902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6de9bffc-8e74-42cb-bc29-3ad955849e16 · outbound

This paper cites Adversar- ial purification with score-based generative models.

Random Sampling for Diffusion-based Adversarial Purification Adversar- ial purification with score-based generative models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.707347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2440d348-b9c6-402e-9a15-7398f84966c9 · outbound

This paper cites FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model.

Random Sampling for Diffusion-based Adversarial Purification FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.521535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4253341d-ac7a-4342-965d-febc2180978a · outbound

This paper cites Wide Residual Networks.

Random Sampling for Diffusion-based Adversarial Purification Wide Residual Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:24.525439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28363145-ab00-4a2e-8f78-f98319332f65 · outbound

This paper cites Xing, Laurent El Ghaoui, and Michael I.

Random Sampling for Diffusion-based Adversarial Purification Xing, Laurent El Ghaoui, and Michael I

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.696695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8c8d29b9-7571-4251-b0b9-78330e834db5 · outbound

This paper cites an unresolved cited work.

Random Sampling for Diffusion-based Adversarial Purification Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:47:24.686054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f9b2110-a597-4c52-94ff-2b9e437a3936 · outbound

This paper cites an unresolved cited work.

Random Sampling for Diffusion-based Adversarial Purification Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:47:24.674348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 370c47b5-573b-4716-aca4-8e9b344280df · outbound

This paper cites Thus, asynchronous attacks are in- troduced to challenge existing diffusion-based purification methods.

Random Sampling for Diffusion-based Adversarial Purification Thus, asynchronous attacks are in- troduced to challenge existing diffusion-based purification methods

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:47:24.663992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.