SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models | Paper Citation Record

Observation 692bdb65-8ca3-48d6-97bf-07bebdfd0d6a · outbound

This paper cites DorPatch: Distributed and occlusion-robust adversarial patch to evade certifiable defenses,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models DorPatch: Distributed and occlusion-robust adversarial patch to evade certifiable defenses,

Reference 1

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Observation 813cb60b-668c-453e-8ca8-b716c4693469 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Imagenet classification with deep convolutional neural networks,

Reference 2

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Observation f69ea120-f8f3-4915-b019-9deb08cbd33e · outbound

This paper cites Deep residual learning for image recognition,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Deep residual learning for image recognition,

Reference 3

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Observation 1c3bf134-dea9-4802-863a-fcf4bda9f554 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 4

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Observation 17493acb-2746-43b0-b2ef-d8361a00567b · outbound

This paper cites Intriguing properties of neural networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Intriguing properties of neural networks,

Reference 5

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This paper cites Explaining and harnessing adversarial examples,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Explaining and harnessing adversarial examples,

Reference 6

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Observation 81b34f5b-5b6f-4e4b-8428-543663f36d81 · outbound

This paper cites Adversarial Patch.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Adversarial Patch

Reference 7

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Observation ef35909a-ed5d-4527-afcf-af9a98c55537 · outbound

This paper cites Lavan: Localized and visible adversarial noise,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Lavan: Localized and visible adversarial noise,

Reference 8

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Observation be140ea1-efa7-41f3-9f78-a6e9965fe9fc · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Robust physical-world attacks on deep learning visual classification,

Reference 9

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Observation 01df4e24-4aa6-4c7b-9515-fb628c4abe85 · outbound

This paper cites DPatch: An Adversarial Patch Attack on Object Detectors.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models DPatch: An Adversarial Patch Attack on Object Detectors

Reference 10

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Observation 9a9a3d12-6c5a-4419-9820-c5bc33a1df50 · outbound

This paper cites Pad: Patch- agnostic defense against adversarial patch attacks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Pad: Patch- agnostic defense against adversarial patch attacks,

Reference 11

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Observation 9b074267-cf51-4e2f-b47c-21d09e377e11 · outbound

This paper cites Certified defenses for adversarial patches,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Certified defenses for adversarial patches,

Reference 12

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Observation 27a21386-2b42-4ac1-bd32-6f65512fc4e9 · outbound

This paper cites {PatchGuard}: A provably robust defense against adversarial patches via small receptive fields and masking,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models {PatchGuard}: A provably robust defense against adversarial patches via small receptive fields and masking,

Reference 13

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Observation 55eaff19-0aef-4ebb-9b17-607f89158e2a · outbound

This paper cites {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier,

Reference 14

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Observation bf4e3836-9d9c-49e0-a8a7-a90d5c7f6ff7 · outbound

This paper cites {PatchCURE}: Improving certifiable robustness, model utility, and computation effi- ciency of adversarial patch defenses,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models {PatchCURE}: Improving certifiable robustness, model utility, and computation effi- ciency of adversarial patch defenses,

Reference 15

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Observation bdcf039b-1f6d-4a0a-a212-fd43bc5873a7 · outbound

This paper cites Defending against adversarial attacks by randomized diversification,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Defending against adversarial attacks by randomized diversification,

Reference 16

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Observation 37cbdbd3-180c-4818-b6a8-cda7af65c514 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Feature squeezing: Detecting adversarial examples in deep neural networks,

Reference 17

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Observation e7409901-ac08-4b82-ab8c-b0bc94c1ea6b · outbound

This paper cites Communication in the presence of noise,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Communication in the presence of noise,

Reference 18

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Observation f45e5a56-b506-4ab4-a3a6-21422451a105 · outbound

This paper cites Fourier features let networks learn high-frequency functions in low-dimensional domains,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Fourier features let networks learn high-frequency functions in low-dimensional domains,

Reference 19

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Observation e25b59a7-ad85-40ad-86b4-01ad13a4e1e3 · outbound

This paper cites Statistics of natural image categories,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Statistics of natural image categories,

Reference 20

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Observation df0e5cb4-10b7-46cc-a823-daad42703e66 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Imagenet: A large-scale hierarchical image database,

Reference 21

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Observation a71156b5-1440-48c9-adf3-08753003d33c · outbound

This paper cites Towards evaluating the robustness of neural networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Towards evaluating the robustness of neural networks,

Reference 22

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Observation 9942dc31-a438-4124-a945-7dd8b532c422 · outbound

This paper cites The security of machine learning,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models The security of machine learning,

Reference 23

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Observation a0f4c822-adc5-48ea-a8d7-785d0051ecdc · outbound

This paper cites Evasion attacks against machine learning at test time,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Evasion attacks against machine learning at test time,

Reference 24

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Observation 588024b9-00c7-44db-9daa-d252a2850d90 · outbound

This paper cites The limitations of deep learning in adversarial settings,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models The limitations of deep learning in adversarial settings,

Reference 25

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Observation f2b9efe8-e4da-45f1-affc-df823d881b50 · outbound

This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Minimally distorted adversarial examples with a fast adaptive boundary attack,

Reference 26

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Observation f7472de9-fc9c-410f-a223-8761dac9d17b · outbound

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

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Towards deep learning models resistant to adversarial attacks,

Reference 27

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Observation cba142cc-135c-4051-9af1-19bbf41b64a2 · outbound

This paper cites On visible adversarial perturbations & digital watermarking,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models On visible adversarial perturbations & digital watermarking,

Reference 28

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Observation b36742a7-cb6f-4dc7-a239-e9cc3e510486 · outbound

This paper cites Adversarial sticker: A stealthy attack method in the physical world,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Adversarial sticker: A stealthy attack method in the physical world,

Reference 29

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This paper cites Patchattack: A black-box texture-based attack with reinforcement learning,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Patchattack: A black-box texture-based attack with reinforcement learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:05.575161Z

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Observation fd2d35d2-676e-4e53-9097-6cbf2ad34c53 · outbound

This paper cites On Physical Adversarial Patches for Object Detection.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models On Physical Adversarial Patches for Object Detection

Reference 31

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Observation 2b1cb77b-7f03-4308-b3a1-3b61b4bc491f · outbound

This paper cites Local gradients smoothing: Defense against localized adversarial attacks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Local gradients smoothing: Defense against localized adversarial attacks,

Reference 32

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Observation f960c475-1cdd-4c7a-ba49-55faa9b80586 · outbound

This paper cites Clipped bagnet: Defending against sticker attacks with clipped bag-of-features,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Clipped bagnet: Defending against sticker attacks with clipped bag-of-features,

Reference 33

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Observation cd5fee64-39b4-4686-bca8-98d032050a1b · outbound

This paper cites Sentinet: Detecting localized universal attacks against deep learning systems,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Sentinet: Detecting localized universal attacks against deep learning systems,

Reference 34

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Observation ecc94b7a-5219-4b74-a5b5-27bd04a1c21f · outbound

This paper cites Jedi: Entropy-based localization and removal of adversarial patches,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Jedi: Entropy-based localization and removal of adversarial patches,

Reference 35

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Observation 3c4234fc-86df-4bb1-a5fd-9446a1af7fbb · outbound

This paper cites Patchzero: Defending against adversarial patch attacks by detecting and zeroing the patch,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Patchzero: Defending against adversarial patch attacks by detecting and zeroing the patch,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:04.850701Z

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Observation 7e4aa260-9e54-4060-9373-71497deb4e2d · outbound

This paper cites Adversarial training against location- optimized adversarial patches,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Adversarial training against location- optimized adversarial patches,

Reference 37

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Observation 964503ef-0b4e-4fc2-9347-80a1fc16f525 · outbound

This paper cites Defending against physically realizable attacks on image classification,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Defending against physically realizable attacks on image classification,

Reference 38

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Observation 257a3cd5-34e5-4d43-98a0-6ae509db6aea · outbound

This paper cites Efficient training methods for achieving adversarial robustness against sparse attacks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Efficient training methods for achieving adversarial robustness against sparse attacks,

Reference 39

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Observation b5e7fc04-8d98-4eb1-bf07-68e3e6fd0d03 · outbound

This paper cites Learning a deep convolu- tional network for image super-resolution,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Learning a deep convolu- tional network for image super-resolution,

Reference 40

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Observation c65b8bb6-d715-401e-a645-8859e20aa6ce · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Accurate image super-resolution using very deep convolutional networks,

Reference 41

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Observation dc784e51-adb6-42a5-a2b0-7db628cff473 · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Photo-realistic single image super-resolution using a generative adversarial network,

Reference 42

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Observation 0b5e29e9-fb3c-48a8-a536-b3423ea67bec · outbound

This paper cites Esrgan: Enhanced super-resolution generative adversar- ial networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Esrgan: Enhanced super-resolution generative adversar- ial networks,

Reference 43

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Observation 646e0551-02fc-4af1-89e6-e64277251a26 · outbound

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

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Image super- resolution as a defense against adversarial attacks,

Reference 44

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Observation dbc64a64-c0f2-493f-be44-3ad245c44d6f · outbound

This paper cites Diffusion Models for Adversarial Purification.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Diffusion Models for Adversarial Purification

Reference 45

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Observation 915f979e-feae-4295-b81e-dc537e266104 · outbound

This paper cites Analysis and comparison of various image downsampling and upsampling methods,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Analysis and comparison of various image downsampling and upsampling methods,

Reference 46

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Observation 6b0551bb-a36f-4af8-b09d-b12c623a8995 · outbound

This paper cites Image quality assessment: From error visibility to structural similarity,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Image quality assessment: From error visibility to structural similarity,

Reference 47

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Observation af14b3a5-91f8-4b78-b8d5-ddea8b0138fe · outbound

This paper cites Countering adver- sarial images using input transformations,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Countering adver- sarial images using input transformations,

Reference 48

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Observation 3eb665bc-bbbb-46f4-afdd-f30a2aa3483d · outbound

This paper cites Real-esrgan: Training real- world blind super-resolution with pure synthetic data,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Real-esrgan: Training real- world blind super-resolution with pure synthetic data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:03.360722Z

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Observation 51bcd8e5-db21-4b19-8fbe-5820b125d8f3 · outbound

This paper cites Diffusion models for adversarial purification,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Diffusion models for adversarial purification,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:03.107266Z

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Observation f156f7c3-10d0-4333-afcd-623ac429a573 · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 51

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Observation fe4ac0a6-e560-498e-a72b-37f7231192d3 · outbound

This paper cites Identity mappings in deep residual networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Identity mappings in deep residual networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:02.801017Z

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Observation 0cb81e37-3f41-47d0-ad4f-0bd0b0583868 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Pytorch: An imperative style, high-performance deep learning library,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:57.413156Z

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Observation 409a27b7-bb46-4272-b568-d62f0280716b · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Ntire 2017 challenge on single image super-resolution: Dataset and study,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:02.496703Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a5c8e13c-bb6d-4e43-9d2a-02ab1cb03e22 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Ntire 2017 challenge on single image super-resolution: Methods and results,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:02.248997Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f0cc8c1-57ee-4d20-84ab-8da6be63bed0 · outbound

This paper cites Recovering realistic texture in image super-resolution by deep spatial feature transform,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Recovering realistic texture in image super-resolution by deep spatial feature transform,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.906592Z

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Observation 13c10796-14ee-4f4d-99b3-957bd35aa9fa · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Very deep convolutional networks for large-scale image recognition,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:57.802575Z

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Observation e18c6ac1-bbbb-42d2-9b2c-b2aacaf6bd7f · outbound

This paper cites Wide residual networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Wide residual networks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.723795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7dcda8d5-b514-4e1e-9443-2ed432d9bd16 · outbound

This paper cites Microsoft coco: Common objects in context,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Microsoft coco: Common objects in context,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.575835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7517dea0-5971-4b38-81a6-1d25bece28df · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.389342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e20000d-4187-43ff-9d68-83808800c71c · outbound

This paper cites Dpatch: An adversarial patch attack on object detectors,.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Dpatch: An adversarial patch attack on object detectors,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.233041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62cd6e0f-b937-4aca-a698-ae42d1d9b9b7 · outbound

This paper cites Adversarial robustness toolbox (art),.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Adversarial robustness toolbox (art),

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:01.021577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 89d28a0d-f985-41fc-a30e-556b930d3861 · outbound

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

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Learning multiple layers of features from tiny images,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:58.443383Z

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Observation 1bb68669-1bce-4c40-a86f-b61dd609a7e8 · outbound

This paper cites Image Super-Resolution via Iterative Refinement.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Image Super-Resolution via Iterative Refinement

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:58.541705Z

Source-reported events for the cited work

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Observation 4f4fbda5-eaba-4982-8491-e9f6b5ff20c9 · outbound

This paper cites This underscoresSuperPure’splug-and-playcapability, as no task-specific retraining is required.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models This underscoresSuperPure’splug-and-playcapability, as no task-specific retraining is required

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:00.759168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3264d57-5500-4da7-a044-d5c4af7a82d9 · outbound

This paper cites Clean detection accuracy of60%plunges to35%under DPatch, but SuperPurerestores it to58%, indicating robust generalization beyond classification tasks.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Clean detection accuracy of60%plunges to35%under DPatch, but SuperPurerestores it to58%, indicating robust generalization beyond classification tasks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:00.482733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80c73725-01f0-4452-9364-77fd4676ca31 · outbound

This paper cites The total adversarial area thus becomes increasingly fragmented, posing a stronger challenge.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models The total adversarial area thus becomes increasingly fragmented, posing a stronger challenge

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:08:00.242830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0c1ad01-c312-4368-9f70-1a9be420fac3 · outbound

This paper cites De- spite the attacker’s full knowledge ofSuperPure, our method 0 1 2 4 80 0.2 0.4 0.6 0.8 1 Number of Patches Robustness SuperPure+ PatchCleanser [14] Fig.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models De- spite the attacker’s full knowledge ofSuperPure, our method 0 1 2 4 80 0.2 0.4 0.6 0.8 1 Number of Patches Robustness SuperPure+ PatchCleanser [14] Fig

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:59.980412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21b69f2e-813c-43d8-b68d-978c311ef8b5 · outbound

This paper cites an unresolved cited work.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:07:59.736835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cfbf5add-dfc7-4519-9e1e-dfb74a5a5b3f · outbound

This paper cites Robustness Trade-off:Our tests show that while SR3 can improve image fidelity slightly, it issignifi- cantly slower.

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models Robustness Trade-off:Our tests show that while SR3 can improve image fidelity slightly, it issignifi- cantly slower

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:59.510409Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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