Paper Citation Record · LEDGER

Robustness Evaluation for Video Models with Reinforcement Learning

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2506.05431.

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

pith.paper-citation-record.v1
2506.05431 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:38:57.475553Z

measured 37 of 37 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 6ad20d1b-0e84-4e8e-84ba-6588be4fb8f3 · outbound

This paper cites Appending adversarial frames for universal video at - tack.

Robustness Evaluation for Video Models with Reinforcement Learning Appending adversarial frames for universal video at - tack

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.548904Z

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Observation 3336ff7c-40fd-4b1a-9381-385c1754de62 · outbound

This paper cites Openmmlab’s next generation video understanding toolbox and benchmark.

Robustness Evaluation for Video Models with Reinforcement Learning Openmmlab’s next generation video understanding toolbox and benchmark

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.312945Z

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Observation 4b914bfc-07e3-4da4-b86f-d937947acb14 · outbound

This paper cites Identifying the key frames: An attention-aware sampling method for action recognition.

Robustness Evaluation for Video Models with Reinforcement Learning Identifying the key frames: An attention-aware sampling method for action recognition

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.040924Z

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Observation 4a27b6eb-f84e-4179-9322-fe65f9cea662 · outbound

This paper cites Slowfast networks for video recognition.

Robustness Evaluation for Video Models with Reinforcement Learning Slowfast networks for video recognition

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.795915Z

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Observation 487d26aa-a1ac-496c-8e84-be0e01d74ba1 · outbound

This paper cites Can spatiotemporal 3d cnns retrace the history of 2d cnns and im- agenet? In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018.

Robustness Evaluation for Video Models with Reinforcement Learning Can spatiotemporal 3d cnns retrace the history of 2d cnns and im- agenet? In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.535057Z

Source-reported events for the cited work

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Observation 0f750b5a-18c0-479d-8ea4-4e8680f48330 · outbound

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

Robustness Evaluation for Video Models with Reinforcement Learning Just one moment: Structural vulnerability of deep action recognition against one frame attack

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.323964Z

Source-reported events for the cited work

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Observation 9a1efff7-7985-4194-a3c5-1ec07d7f5f3b · outbound

This paper cites Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors.

Robustness Evaluation for Video Models with Reinforcement Learning Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors

Reference 7

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Observation 11a1a34b-c9c4-4e18-8114-987f7f270527 · outbound

This paper cites Black-box adversarial attacks on video recog- nition models.

Robustness Evaluation for Video Models with Reinforcement Learning Black-box adversarial attacks on video recog- nition models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.121820Z

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Observation de11879c-6a53-4cb6-a371-a03e8a023e0a · outbound

This paper cites Hmdb: a large video database for human motion recognition.

Robustness Evaluation for Video Models with Reinforcement Learning Hmdb: a large video database for human motion recognition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:02.891431Z

Source-reported events for the cited work

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Observation de4bf0f2-42c6-4ce6-9989-1a6358c1221a · outbound

This paper cites Adversarial Perturbations Against Real-Time Video Classification Systems.

Robustness Evaluation for Video Models with Reinforcement Learning Adversarial Perturbations Against Real-Time Video Classification Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:38:54.143096Z

Source-reported events for the cited work

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Observation 16dd183c-27ce-4281-a11a-0dd2dd24c91c · outbound

This paper cites Adversarial attacks on black box video classifiers: Leveraging the power of geometric transformations.

Robustness Evaluation for Video Models with Reinforcement Learning Adversarial attacks on black box video classifiers: Leveraging the power of geometric transformations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:02.625804Z

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.

Observation 15333dc1-5fa6-4e29-9a09-b7a884dd91c5 · outbound

This paper cites Tsm: Temporal shift module for efficient video understanding.

Robustness Evaluation for Video Models with Reinforcement Learning Tsm: Temporal shift module for efficient video understanding

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:02.330263Z

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.

Observation 6f49582b-5e85-46b8-b221-700f0a24aabf · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Robustness Evaluation for Video Models with Reinforcement Learning Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 13

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Observation d8e29486-44bc-491e-8bc4-0a26509f3c52 · outbound

This paper cites Measuring robustness with black-box adversarial attack using reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Measuring robustness with black-box adversarial attack using reinforcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:02.087447Z

Source-reported events for the cited work

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Observation a35eb3c6-081e-4c2a-ad92-92a3bae11d8b · outbound

This paper cites Rl-cam: Visual explana- tions for convolutional networks using reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Rl-cam: Visual explana- tions for convolutional networks using reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.937341Z

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.

Observation 85de8847-6d4b-42f9-ba7d-6216eab7b6b0 · outbound

This paper cites Robustness with query - efficient adversarial attack using reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Robustness with query - efficient adversarial attack using reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.752142Z

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

Observation c862d825-28f9-4211-8bf7-06b90af16082 · outbound

This paper cites Reinforcement learning based black -box adversarial attack for robustness improvement.

Robustness Evaluation for Video Models with Reinforcement Learning Reinforcement learning based black -box adversarial attack for robustness improvement

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.597303Z

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.

Observation e3ab6c9c-e050-4a9d-b876-2eef09170272 · outbound

This paper cites Robustness with black-box adversarial attack using reinforce- ment learning.

Robustness Evaluation for Video Models with Reinforcement Learning Robustness with black-box adversarial attack using reinforce- ment learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.461665Z

Source-reported events for the cited work

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Observation 29d9e769-3006-44f8-8932-46477d657918 · outbound

This paper cites Benchmark generation framework with customizable distortions for image classifier robustness.

Robustness Evaluation for Video Models with Reinforcement Learning Benchmark generation framework with customizable distortions for image classifier robustness

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.275245Z

Source-reported events for the cited work

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Observation 3f00dc72-f9ba-4b66-8a5e-c57922c9c35d · outbound

This paper cites Robustness and visual explanation for black box image, video, and ecg signal classification with reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Robustness and visual explanation for black box image, video, and ecg signal classification with reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.143720Z

Source-reported events for the cited work

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Observation f0856d4b-634a-4b87-baec-e775ac07d198 · outbound

This paper cites Reinforcement learning platform for adversarial black - box attacks with custom distortion filters.

Robustness Evaluation for Video Models with Reinforcement Learning Reinforcement learning platform for adversarial black - box attacks with custom distortion filters

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:01.029136Z

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.

Observation a86f5649-fce7-48ce-b4f5-4c614a720fe4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Robustness Evaluation for Video Models with Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 22

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Observation 1e0138bd-f083-4c2e-ab86-27e8e3b61eb8 · outbound

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

Robustness Evaluation for Video Models with Reinforcement Learning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 23

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Observation f59c96ae-b804-429b-9bfc-ba38f22870e0 · outbound

This paper cites Autoattacker: A reinforcement learning approach for black - box adversarial attacks.

Robustness Evaluation for Video Models with Reinforcement Learning Autoattacker: A reinforcement learning approach for black - box adversarial attacks

Reference 24

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Observation 1b192b94-68a6-46cf-9a4c-c77454f18567 · outbound

This paper cites Temporal segment networks: Towards good practices for deep action recognition.

Robustness Evaluation for Video Models with Reinforcement Learning Temporal segment networks: Towards good practices for deep action recognition

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:00.724176Z

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.

Observation b7e30d38-c6ce-459a-8880-6ed4a209cd9c · outbound

This paper cites Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models.

Robustness Evaluation for Video Models with Reinforcement Learning Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models

Reference 26

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Observation 61d24a44-59ae-4cd7-ac6f-ffd0567007b7 · outbound

This paper cites Sparse adversarial perturbations for videos.

Robustness Evaluation for Video Models with Reinforcement Learning Sparse adversarial perturbations for videos

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:00.593220Z

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.

Observation 3fa994e7-38f1-489e-be78-5220d040ea76 · outbound

This paper cites Sparse black - box video attack with reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Sparse black - box video attack with reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:00.455153Z

Source-reported events for the cited work

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Observation ff2cbbca-682d-4c26-ad3c-86279c31dba9 · outbound

This paper cites Effi- cient robustness assessment via adversarial spatial -temporal focus on videos.

Robustness Evaluation for Video Models with Reinforcement Learning Effi- cient robustness assessment via adversarial spatial -temporal focus on videos

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:00.142463Z

Source-reported events for the cited work

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Observation 5f518c7c-4163-4dda-9ec1-2c9bb01265c6 · outbound

This paper cites Heuristic black-box adversarial attacks on video recognition models.

Robustness Evaluation for Video Models with Reinforcement Learning Heuristic black-box adversarial attacks on video recognition models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:59.883501Z

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.

Observation 18717c4e-31da-4464-a967-80409860653e · outbound

This paper cites Towards transferable adversarial attacks on vision transformers.

Robustness Evaluation for Video Models with Reinforcement Learning Towards transferable adversarial attacks on vision transformers

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:59.629630Z

Source-reported events for the cited work

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Observation e426f7e1-1048-413d-b4a2-680e4269080a · outbound

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

Robustness Evaluation for Video Models with Reinforcement Learning Boosting the transferability of video adversarial examples via temporal translation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:59.383625Z

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.

Observation e2545d8d-6309-49fc-9cd3-16af3a596b5e · outbound

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

Robustness Evaluation for Video Models with Reinforcement Learning Cross-modal transferable adversarial attacks from images to videos

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:59.142975Z

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.

Observation e3ccda07-3d16-43c6-ab6d-ba4c746ae255 · outbound

This paper cites Efficient sparse attacks on videos using reinforcement learning.

Robustness Evaluation for Video Models with Reinforcement Learning Efficient sparse attacks on videos using reinforcement learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:58.899692Z

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.

Observation c669ef2f-a713-4b5f-bbaa-95a828c23103 · outbound

This paper cites Cube-evo: A query-efficient black-box attack on video classification system.

Robustness Evaluation for Video Models with Reinforcement Learning Cube-evo: A query-efficient black-box attack on video classification system

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:58.591096Z

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.

Observation e60cf38c-084d-4b37-87a2-96c2e86ca836 · outbound

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

Robustness Evaluation for Video Models with Reinforcement Learning Motion- excited sampler: Video adversarial attack with sparked prior

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:58.350789Z

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.

Observation 09c5b8f2-ccf1-4e1c-88c9-6eb592efaecc · outbound

This paper cites Deep reinforce- ment learning for unsupervised video summarization with diversity-representativeness reward.

Robustness Evaluation for Video Models with Reinforcement Learning Deep reinforce- ment learning for unsupervised video summarization with diversity-representativeness reward

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:38:58.087934Z

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Pith citing papers

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