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Paper Citation Record · LEDGER

VideoPure: Diffusion-based Adversarial Purification for Video Recognition

As of 11 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2501.14999.

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

pith.paper-citation-record.v1
2501.14999 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:48:57.619008Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

64 of 64 outbound references displayed

  • verified exact0
  • verified fuzzy51
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce74e9e5-f2a1-4034-b3ed-e4c54069281a · outbound

This paper cites Deep residual learning for image recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Deep residual learning for image recognition,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e92dbc40-34a6-47f9-89e4-82ebe7fe8221 · outbound

This paper cites Towards practical certifiable patch defense with vision transformer,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Towards practical certifiable patch defense with vision transformer,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.669426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.334819Z digest=sha256:6f2e9169173b5dea09664eb82ed9861c113a497bde56449e9355758e8dc25ff8

Observation ad3565e6-3f26-4ca4-812e-354f2eec6c19 · outbound

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

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Towards deep learning models resistant to adversarial attacks,

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 02b75289-9c15-4f44-81ba-311dcdb6192b · outbound

This paper cites Cross-shaped adversarial patch attack,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Cross-shaped adversarial patch attack,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.637106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.344569Z digest=sha256:38e19ed7eddd0884b7d0890ff29833d0bd85f2b5c8c65b2b18d0930618843374

Observation 9497e1c2-7da0-4476-9834-bf99c57dff8b · outbound

This paper cites Adversarial attacks on video object segmentation with hard region discovery,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Adversarial attacks on video object segmentation with hard region discovery,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.620066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.349247Z digest=sha256:fe01190a59101c7e1db7f0cd1d8ebcf6594c0190d75a2b9be1d909ef627ccbe8

Observation 8f0c7a96-9392-4298-ba4a-eb96fd676cc0 · outbound

This paper cites Diffusion patch attack with spatial-temporal cross-evolution for video recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Diffusion patch attack with spatial-temporal cross-evolution for video recognition,

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 49e0b7d9-6488-4fcb-8679-2abf6ec3e5ac · outbound

This paper cites Bullet-screen-emoji attack with temporal difference noise for video action recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Bullet-screen-emoji attack with temporal difference noise for video action recognition,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.586453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.359764Z digest=sha256:b64a952b6085712ee2c53a2bea9ad1a6e625528634f71efa41508d68d767b081

Observation 8fcbc192-7b04-4a68-8beb-57b913d8517a · outbound

This paper cites Only once attack: Fooling the tracker with adversarial template,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Only once attack: Fooling the tracker with adversarial template,

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.364231Z digest=sha256:77a89597684c91a03a827383c820cd4c14bf7f25faf8ab1a5659d2a4e04fb3c5

Observation 6c0e77df-854e-4956-aaf7-e3404bb8fd2f · outbound

This paper cites A simple and strong baseline for universal targeted attacks on siamese visual tracking,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition A simple and strong baseline for universal targeted attacks on siamese visual tracking,

Reference 9

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raw_fallback, observed 2026-08-10T14:48:58.556944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.368370Z digest=sha256:7d454bb5a2637c35bc725f4310154ff7da4026ab30a2a985f5ecb73c713293f8

Observation 91544af2-f545-49ea-a4a7-452e6308deef · outbound

This paper cites Sparse adversarial perturbations for videos,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Sparse adversarial perturbations for videos,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f5676331-6795-4ed4-bd1b-a4906b0c525c · outbound

This paper cites The apolloscape dataset for autonomous driving,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition The apolloscape dataset for autonomous driving,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 133fc542-7941-48ab-9e9d-8e6a566389e4 · outbound

This paper cites A system for video surveillance and monitoring,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition A system for video surveillance and monitoring,

Reference 12

Resolution
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raw_fallback, observed 2026-08-10T14:48:58.505750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.381108Z digest=sha256:fea7ebb3abace8c1e3591039a77313fe47f4558f3deec5bf42432a312991d6ab

Observation eb968ab7-43be-4ce5-a00b-8ccd74f19c67 · outbound

This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5d9a704f-a082-4713-88e7-ca82318b078e · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Theoretically principled trade-off between robustness and accuracy,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.489613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.389722Z digest=sha256:4609fbbcd22e7171c7b4bee78a7eda33da8860bf8670562c7b5e3f08ba57c0b6

Observation 830f13f9-bfd9-4db8-93cf-a71005cdc3c4 · outbound

This paper cites Improving robustness using generated data,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Improving robustness using generated data,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.475614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.394689Z digest=sha256:dd7cf3346047964a9598a1c4bcddcd2d126ca28f54daace4ea741e39747ee52f

Observation b20b4910-9da7-400f-9a59-f7aa56c4a8cd · outbound

This paper cites Adversarial machine learning at scale,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Adversarial machine learning at scale,

Reference 16

Resolution
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raw_fallback, observed 2026-08-10T14:48:58.461034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.398954Z digest=sha256:7d7123e88c306b2cc4fcd17b6dc58343ddfaf5ef785e3875a2da161aa074a7c8

Observation b5100662-bed8-4544-924d-54470ded858d · outbound

This paper cites Defending against multiple and unforeseen adversarial videos,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Defending against multiple and unforeseen adversarial videos,

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 2b8edc4b-b9c8-454e-9496-137fff938037 · outbound

This paper cites Analysis and extensions of adversarial training for video classification,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Analysis and extensions of adversarial training for video classification,

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.408468Z digest=sha256:3f7beef6cc6cc92e78550df731fae2faa42eab0b82756dbb67687617ce175c3e

Observation 488c8c5b-2a2d-4fc6-befa-8f876e16e857 · outbound

This paper cites Defense-gan: Protecting classifiers against adversarial attacks using generative models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Defense-gan: Protecting classifiers against adversarial attacks using generative models,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.413487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7a07c090-283a-4e95-8faf-3da2cfa24340 · outbound

This paper cites Pixelde- fend: Leveraging generative models to understand and defend against adversarial examples,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Pixelde- fend: Leveraging generative models to understand and defend against adversarial examples,

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.418585Z digest=sha256:aa0fd1ce8ee4596c88474d048c7e0d56d1dcc59b7345f85393e75502a3f5d459

Observation 0fe90af3-bc98-49b5-b5fb-6cc7c79f7ff3 · outbound

This paper cites Defending video recognition model against adversarial perturbations via defense patterns,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Defending video recognition model against adversarial perturbations via defense patterns,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.379586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.423518Z digest=sha256:ac407b5be976199e74424936adab48e6190f1764cde7bebdc79f3758bdb61dc1

Observation 0d72511e-38a0-41af-a0e1-0d96cd06c6cc · outbound

This paper cites Shield: Fast, practical defense and vaccination for deep learning using jpeg compression,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Shield: Fast, practical defense and vaccination for deep learning using jpeg compression,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.365738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.427711Z digest=sha256:ac5c7d6db2da87d76ad5bf5b27929dac3002cb5cd56b9ac171a7c5811ac76255

Observation 2e4d48f9-c39f-4d60-8ac3-59b2c6a001f7 · outbound

This paper cites Defense for adversarial videos by self-adaptive jpeg compression and optical texture,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Defense for adversarial videos by self-adaptive jpeg compression and optical texture,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.350476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.431824Z digest=sha256:349b87d4ccad432855565c1a6ff4c437a994314881d5310c724e4bdb18fbf427

Observation 89633f17-8433-4423-a41e-825893951d1d · outbound

This paper cites Temporal shuffling for defending deep action recognition models against adver- sarial attacks,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Temporal shuffling for defending deep action recognition models against adver- sarial attacks,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.333445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.435824Z digest=sha256:ac46ef55ccc155e3bbd2ab69eca405dd0fd1cdb672176dace6f3b6346237e2ae

Observation f9060b8d-bd6b-4611-bd21-00c290314358 · outbound

This paper cites Diffusion models for adversarial purification,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Diffusion models for adversarial purification,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.314454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.440191Z digest=sha256:d7254d27cbae1b536130a873d8a8d45741544ff30528f536837a77932d2c9d6a

Observation c7d0149b-7ce5-40ae-99df-d0dba432683a · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.295305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.444811Z digest=sha256:c42b26696f0d51cb4f3eba1896311453e450cad0a3aa394cb25f30a1cd4b073e

Observation 37876f57-f152-4c3b-ac96-145716e92f54 · outbound

This paper cites Synthesizing robust adversarial examples,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Synthesizing robust adversarial examples,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.277100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.449318Z digest=sha256:2f07797de116bbb3a5e157deefaa743765ac28501a4df83c2f2a58db495cf78e

Observation 1f331525-e024-4651-8342-f81ad2e64319 · outbound

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

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.258556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.454426Z digest=sha256:be7c8ceeea46e405cfec57f92c721d378ab7ee55348187e2eb0b58b27faec161

Observation 578b60d3-52e2-4774-9de7-25b2820b3bf6 · outbound

This paper cites Threat Model-Agnostic Adversarial Defense using Diffusion Models.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Threat Model-Agnostic Adversarial Defense using Diffusion Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.459111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.459111Z digest=sha256:9a6f8be3431c9b8be0b30f84a97054d3a097315970227f6b76f4688f936f9995

Observation 3bfe0234-0321-40ff-b2ca-0c8d819a56b8 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification from Random Noise.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Guided Diffusion Model for Adversarial Purification from Random Noise

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.463696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.463696Z digest=sha256:bf66bdebf1f436a131a31c7dc87f1db00a66abff6d5d592afc0e53dbe2db1d9d

Observation 98d7040a-20fe-4181-a01a-afb4509652d0 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Guided Diffusion Model for Adversarial Purification

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.468719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.468719Z digest=sha256:bfce7e3a441a65faffb05d0bb0a35088a3c0903485e21bceb22d84e5a46764f9

Observation 4c3eede9-0cee-447b-83d6-3bc58ace29d0 · outbound

This paper cites Enhancing adversarial robustness via score-based optimization,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Enhancing adversarial robustness via score-based optimization,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.241788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.473409Z digest=sha256:c665ab73062fb4e0d50e954b5fb34939615a78eb1b2a125551c37ea835edee6b

Observation 83f75e84-2df0-4406-8c81-e17f93a8fde3 · outbound

This paper cites Denoising diffusion probabilistic models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Denoising diffusion probabilistic models,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.222890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.478294Z digest=sha256:60e5248704faeabfc8ebf5d1e4f93e8cd68f48bb5b65f8cfce3b0f98dd5f57b3

Observation a3151141-35fe-4e00-b640-99f8254924d9 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition ModelScope Text-to-Video Technical Report

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.482864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.482864Z digest=sha256:0556b30a82ce3fedae02790d286dc0c22219b3d25523a64b2748b3ca6748bd63

Observation 60c4372d-858e-465d-b9ac-c218c9bed738 · outbound

This paper cites Denoising diffusion implicit models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Denoising diffusion implicit models,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.206012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.487636Z digest=sha256:244ccb53adaec12ba9e1e6a8755c93184dfe388811438310514bfdae0eecc17f

Observation 95532c82-d9c2-4d24-b144-dc2e80482ff5 · outbound

This paper cites Intriguing properties of neural networks,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Intriguing properties of neural networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.188661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.491658Z digest=sha256:aa5dffa48427c3d61ddb8bc460b65ffec2be7f81f44c91326671dc28bd2d5ba2

Observation db155556-5feb-4238-809e-a250f2cd945f · outbound

This paper cites Boosting the transferability of video adversarial examples via temporal translation,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Boosting the transferability of video adversarial examples via temporal translation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.171398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.495539Z digest=sha256:bb65d6a3a6873974b8d6a148e9066e062a30165b8ba62536b994267d943f7864

Observation d0ad1e5c-bc92-45b0-9b8a-577d00719179 · outbound

This paper cites Global-local characteristic excited cross-modal attacks from images to videos,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Global-local characteristic excited cross-modal attacks from images to videos,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.154418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.499380Z digest=sha256:a340aaacd4a6d6e9b43ff484e6c45c6146eebf49fba24b1d4ce85d16e018382f

Observation ab761e5e-a164-4f71-b869-7c0cc7b55149 · outbound

This paper cites Cross-modal transferable adversarial attacks from images to videos,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Cross-modal transferable adversarial attacks from images to videos,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.134388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.504157Z digest=sha256:54050285e0e0d545b27b7b9412b8635477351a2e35e325c2e4d2c4700b5537fa

Observation f95ea63a-935e-413c-a7f1-89a375810665 · outbound

This paper cites Black-box adversarial attacks on video recognition models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Black-box adversarial attacks on video recognition models,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.116133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.509033Z digest=sha256:035c9fe3c742bf59118709839a6d3c37e8f0309be7cc0ffa0fee2e15418473f0

Observation 1b83e8db-c612-4f76-b4d6-3702753380ce · outbound

This paper cites Motion-excited sampler: Video adversarial attack with sparked prior,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Motion-excited sampler: Video adversarial attack with sparked prior,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.098925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.513536Z digest=sha256:42771d5605af1d4a3a21045c4c7327051a6b83a83d055b573e9c8753e0b788bd

Observation 812631fd-21ee-4cb3-8988-ec8a5b2a9c60 · outbound

This paper cites Efficient decision-based black-box patch attacks on video recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Efficient decision-based black-box patch attacks on video recognition,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.071532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.518273Z digest=sha256:e4ac943b8f2c71e2d36ba6e3e8ebd7dc1af6bd8a22fa0939032c21cf6984c40e

Observation 09b9773d-46a5-4c5e-999d-faaba698a456 · outbound

This paper cites Towards decision-based sparse attacks on video recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Towards decision-based sparse attacks on video recognition,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.056144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.522720Z digest=sha256:0f06f9ced035d751fb43f2f8913a07814bf4ee96de9bd2f80d24504f28a4f585

Observation 86f26413-c8ea-4ccb-a275-5f118b86e20e · outbound

This paper cites Adversarial examples in the physical world,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Adversarial examples in the physical world,

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.527681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.527681Z digest=sha256:302d773eaa492a7c85bafceacceaaf095353645618dc4bdb61982227c50d4c34

Observation 8ac818fc-f7ef-4a05-b5d2-2cee7034d8bc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Adam: A Method for Stochastic Optimization

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.531914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.531914Z digest=sha256:dbc75ad22c3d8dbbcf3c1e389304c298fb91ca5a110dc889bb96de33f1d8c6e1

Observation a2cafcf9-308e-4281-9539-f1f6b0d9a1a3 · outbound

This paper cites Just one moment: Structural vulnerability of deep action recognition against one frame attack,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Just one moment: Structural vulnerability of deep action recognition against one frame attack,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.030968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.536448Z digest=sha256:66d475103fb23373b354c0afa56f2255f2b1906df75465558d3f693a4bb646be

Observation 79179315-2d22-49b0-a65e-bf9949b6b640 · outbound

This paper cites Over-the-air adversarial flickering attacks against video recognition networks,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Over-the-air adversarial flickering attacks against video recognition networks,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.015521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.540858Z digest=sha256:e089d56000945c47dd46a87aa872316714b2fe8ac52d8ac42ad92742597ec99f

Observation 67e50031-3515-45e6-a9e7-7bbbf327ee20 · outbound

This paper cites Diffattack: Evasion attacks against diffusion-based adversarial purification,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Diffattack: Evasion attacks against diffusion-based adversarial purification,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:58.000207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.545212Z digest=sha256:5229cd181e506b1e3c39a3079a1153e0ebb3fbbc648ec20a0e11f63376a5f160

Observation 37c71e83-9c3f-4caa-85c3-3dcb4bb3b37e · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Explaining and Harnessing Adversarial Examples

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.550419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.550419Z digest=sha256:f54b4a2b07cfff54ab264450add131a8d68ec1b7e5e20c65495e73eef5883015

Observation b3ff5745-6739-4af7-970b-014bd0917e3a · outbound

This paper cites Image super- resolution as a defense against adversarial attacks,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Image super- resolution as a defense against adversarial attacks,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.985129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.555100Z digest=sha256:cec49709f504937c2ee6510723f0c96d66e9351183b5c206557ce0164b733be4

Observation bca7f215-d617-4df5-b449-186893f4c617 · outbound

This paper cites Taming transformers for high- resolution image synthesis,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Taming transformers for high- resolution image synthesis,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.970100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.559572Z digest=sha256:6aa741bc0458218e64b063ec8f8f284ec75ed161975cc69ca9be9a287b38894b

Observation da685d1a-d509-4a33-9dd1-88fec0b708fa · outbound

This paper cites Null- text inversion for editing real images using guided diffusion models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Null- text inversion for editing real images using guided diffusion models,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.955058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.564313Z digest=sha256:0b1e51150acac86acf7b8b02e2d67dd3c999ef51b61843543cbf862b447b2098

Observation fc398202-6ca9-4b7e-88ce-7f37fe850eff · outbound

This paper cites Content-based unrestricted adversarial attack,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Content-based unrestricted adversarial attack,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.938848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.568782Z digest=sha256:87d362ce33862f4ba3c246c2049339f088f31d697161f32596ec5957ca345f3a

Observation bed7f4d2-731e-496e-822c-70122ea83b3c · outbound

This paper cites Diffusion Model-Based Image Editing: A Survey.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Diffusion Model-Based Image Editing: A Survey

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.572647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.572647Z digest=sha256:097b21c732b48913c348e62064d2795129b8e8c5d89cdfbb9d87799152472b18

Observation ce323eeb-4c3f-4eb0-ac86-743b53891540 · outbound

This paper cites Text2video-zero: Text-to-image diffusion models are zero-shot video generators,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Text2video-zero: Text-to-image diffusion models are zero-shot video generators,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.924278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.576617Z digest=sha256:64c3879052387119b8e33e83377f947e32032707e697112e36eae46ff0d3f379

Observation b02063a6-45da-43ad-b06e-3e6b5154587c · outbound

This paper cites Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.580422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.580422Z digest=sha256:0a4d319a3bb29266b2df9d3a4d806283249ef8afc2d08fc8593522e49b7bff27

Observation 4843206b-ff90-4d7e-b130-353c9abe9257 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.585048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.585048Z digest=sha256:3774265235d212a7dc8c8cb946f9b3e0db528f9a84a9fe3592435ddc3409bd89

Observation cfd6c725-14a9-4c57-ba15-4243fccb68c7 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.908948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.590208Z digest=sha256:0c36d63c7d82da490e006753c455e974f786f94d533a762f8eb20801c8cb9b35

Observation 4d4a0b40-a8a0-4b21-b848-4f4035e3ad0c · outbound

This paper cites Non-local neural net- works,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Non-local neural net- works,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.893091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.596139Z digest=sha256:88bf7d277cbc31cc91bfcb66e9d512320ec9a8d7a79929de082217caa4aa3bff

Observation bb5cfc41-af0d-4fda-9d2a-180ad669201a · outbound

This paper cites Slowfast networks for video recognition,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Slowfast networks for video recognition,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.878183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.601999Z digest=sha256:522a547d4edf6dfef54253ef6084926124eb929396b5defb253059d5d9be641e

Observation fd8e2888-cca2-438e-85f7-a07cbc9fb49c · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Raft: Recurrent all-pairs field transforms for optical flow,

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.606320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.606320Z digest=sha256:63463f60322dcb1e16e63e0f323ba4bb26f78431a7324d73cbd50af39e3e6562

Observation 8f8830a6-f160-4fc2-9c37-6d4d37920a81 · outbound

This paper cites Boosting Adversarial Attacks with Momentum.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Boosting Adversarial Attacks with Momentum

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-10T14:48:57.610259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:48:57.610259Z digest=sha256:8f4dcdeac6638bc6c6531425ff252b378a1a08b3e640d7df6f3fa296b334d877

Observation ad82d70f-36ea-404b-8fc5-15119aa451c3 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition High- resolution image synthesis with latent diffusion models,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.852970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.614469Z digest=sha256:f960a7a2af06d2876bf7b70fb41f557b628dc5727f25306591366e47f13fbc40

Observation a3739b80-1aad-4e3c-b9ac-91410fbe8e90 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

VideoPure: Diffusion-based Adversarial Purification for Video Recognition Diffusion models beat gans on image synthesis,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:48:57.836319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:48:57.619008Z digest=sha256:be8fa06ebad62d733692b76eacbd410b5d7ec39821ceaaf91ee8a0267f1a057d

Pith citing papers

No inbound Pith citation observations are available.