Paper Citation Record · LEDGER

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2509.07495.

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

pith.paper-citation-record.v1
2509.07495 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:10:18.181350Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-08T04:29:57.545703Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T04:34:31.225225Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
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External citation measurements

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Outbound references

Observation dd77c984-b275-409a-b2ac-fcd77a5d18ee · outbound

This paper cites Fine- grained object recognition and zero-shot learning in remote sensing imagery.IEEE Transactions on Geoscience and Remote Sensing, 56(2):770–779, 2017.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Fine- grained object recognition and zero-shot learning in remote sensing imagery.IEEE Transactions on Geoscience and Remote Sensing, 56(2):770–779, 2017

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.514904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5841a017-41bf-4426-8bb1-03c55e139270 · outbound

This paper cites Progressive learning vision transformer for open set recognition of fine-grained objects in remote sensing images.IEEE Transactions on Geoscience and Remote Sensing, 61:1–13, 2023.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Progressive learning vision transformer for open set recognition of fine-grained objects in remote sensing images.IEEE Transactions on Geoscience and Remote Sensing, 61:1–13, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.506922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f08600b6-4370-4800-b6d4-318ddfc580a8 · outbound

This paper cites Transferable adversarial attacks for remote sensing object recognition via spatial-frequency co-transformation.IEEE Transactions on Geoscience and Remote Sensing, 2024.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Transferable adversarial attacks for remote sensing object recognition via spatial-frequency co-transformation.IEEE Transactions on Geoscience and Remote Sensing, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.498976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a678b24b-98a0-43d7-a1f8-cc37431e806a · outbound

This paper cites Adversarial examples in the physical world.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Adversarial examples in the physical world

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.083250Z

Source-reported events for the cited work

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Observation 81352057-e9b7-47ef-8eaa-4a61ead7e243 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Explaining and Harnessing Adversarial Examples

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.085898Z

Source-reported events for the cited work

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Observation 31842cbe-d0a2-408f-87f3-c407301f4dac · outbound

This paper cites Improvingtransferabilityofadversarial examples with input diversity.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Improvingtransferabilityofadversarial examples with input diversity

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.485794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32a71edd-609b-406a-b3db-ea840e481ca4 · outbound

This paper cites Patch-wiseattackforfoolingdeepneuralnetwork.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Patch-wiseattackforfoolingdeepneuralnetwork

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.477297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36179878-a919-4fbd-8280-f1ebb2fb96b8 · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.468854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ee085a2-49c5-4a96-af5f-1033e0a84a82 · outbound

This paper cites Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.098494Z

Source-reported events for the cited work

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Observation 7119bf5b-a002-4490-b9d0-24c1f15fec72 · outbound

This paper cites Frequency domain model augmentation for adversarial attack.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Frequency domain model augmentation for adversarial attack

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.101598Z

Source-reported events for the cited work

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Observation ce630db7-7c12-468b-86cd-4dc9f79e9739 · outbound

This paper cites Boosting adversarial attacks with momentum.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Boosting adversarial attacks with momentum

Reference 13

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Observation 41df8d43-77ba-407f-ba9f-9ab802962e95 · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.106127Z

Source-reported events for the cited work

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Observation 6e2e1a69-9e5b-424d-95bc-2b669bbb0e43 · outbound

This paper cites Transferable adversarial perturbations.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Transferable adversarial perturbations

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.449899Z

Source-reported events for the cited work

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Observation 55d0609b-358d-4b45-9527-bf8c2b8e81d1 · outbound

This paper cites an unresolved cited work.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:10:18.442655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 320019fd-679f-47ec-9006-b30bbb083ede · outbound

This paper cites Boosting the transfer- abilityofadversarialexamplesvialocalmixupandadaptivestepsize.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Boosting the transfer- abilityofadversarialexamplesvialocalmixupandadaptivestepsize

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.435372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9340b90c-77f7-46be-aeeb-b2883ce65e89 · outbound

This paper cites Towards transferable targeted attack.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Towards transferable targeted attack

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.428037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83176918-d697-404c-bf3a-8c3cac5ec304 · outbound

This paper cites an unresolved cited work.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-04T22:10:18.413021Z

Source-reported events for the cited work

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Observation 95ae1712-bf46-47e2-bd5c-b152484297a9 · outbound

This paper cites An empirical study of adversarial examples on remote sensing image scene classification.IEEE Transactions on Geoscience and Remote Sensing, 59(9):7419–7433, 2021.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition An empirical study of adversarial examples on remote sensing image scene classification.IEEE Transactions on Geoscience and Remote Sensing, 59(9):7419–7433, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.420994Z

Source-reported events for the cited work

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Observation 5c6b01eb-6be6-4936-a384-8b0d58015d59 · outbound

This paper cites Multifeature collaborative adver- sarialattackinmultimodalremotesensingimageclassification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Multifeature collaborative adver- sarialattackinmultimodalremotesensingimageclassification.IEEE Transactions on Geoscience and Remote Sensing, 60:1–15, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.405616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85db5aae-3f44-484d-b824-56d2b864ea9a · outbound

This paper cites Universaladversarialexamplesin remotesensing:Methodologyandbenchmark.IEEETransactionson Geoscience and Remote Sensing, 60:1–15, 2022.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Universaladversarialexamplesin remotesensing:Methodologyandbenchmark.IEEETransactionson Geoscience and Remote Sensing, 60:1–15, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.397645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8611edd1-ebe4-4cb3-aa8f-25d59ca1255b · outbound

This paper cites Admix: Enhancing the transferability of adversarial attacks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Admix: Enhancing the transferability of adversarial attacks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.390012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18bc4b6e-2829-4b3e-bfc8-8b33653ccf65 · outbound

This paper cites On success and simplicity: A second look at transferable targeted attacks.Advances in Neural Information Processing Systems, 34:6115–6128, 2021.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition On success and simplicity: A second look at transferable targeted attacks.Advances in Neural Information Processing Systems, 34:6115–6128, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.382443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d4fa4df7-949a-4270-82cf-f1e6c646dbcc · outbound

This paper cites Approximating the gradient of cross-entropy loss function.IEEE access, 8:111626– 111635, 2020.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Approximating the gradient of cross-entropy loss function.IEEE access, 8:111626– 111635, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.374817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 904c79e1-b2ec-4e79-8e07-469c8d429aee · outbound

This paper cites Wildpatterns:Tenyearsaftertherise of adversarial machine learning.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Wildpatterns:Tenyearsaftertherise of adversarial machine learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.366856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1139bf0f-745d-4168-9fa8-c49e94c7601e · outbound

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

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 28

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Source-reported events for the cited work

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Observation 3f7377b4-737c-4a2c-aca7-4ec384858056 · outbound

This paper cites Practical black-box attacks against machine learning.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Practical black-box attacks against machine learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.359190Z

Source-reported events for the cited work

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Observation 8912b47f-7669-4ddf-995d-2ceab8a8bdf4 · outbound

This paper cites Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Reference 30

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Observation e249a877-2773-43de-a7c0-f2f8a48ef510 · outbound

This paper cites Boosting adversarial transferability by block shuffle and rotation.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Boosting adversarial transferability by block shuffle and rotation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.350869Z

Source-reported events for the cited work

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Observation f0cecf47-722f-4ce0-898f-7ffcc216fe91 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Distillation as a defense to adversarial perturbations against deep neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.342882Z

Source-reported events for the cited work

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Observation 76991a09-b9c3-4cc0-9436-ec8589b15082 · outbound

This paper cites Towards achieving adversarial robustness by enforcingfeatureconsistencyacrossbitplanes.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Towards achieving adversarial robustness by enforcingfeatureconsistencyacrossbitplanes

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.335149Z

Source-reported events for the cited work

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Observation e415110f-88eb-497f-9392-7837325d7dc3 · outbound

This paper cites Lfc-unet: learned lossless medical image fast compression with u-net.PeerJ Computer Science, 10:e1924, 2024.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Lfc-unet: learned lossless medical image fast compression with u-net.PeerJ Computer Science, 10:e1924, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.327216Z

Source-reported events for the cited work

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Observation 6e8ad9be-f1a7-4bed-a3da-dd00d0fd8c4a · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Ensemble Adversarial Training: Attacks and Defenses

Reference 35

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Observation d8b498b8-3f70-4a4e-babe-2fa88bcefd26 · outbound

This paper cites Adversarial Logit Pairing.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Adversarial Logit Pairing

Reference 36

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Source-reported events for the cited work

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Observation 5c77ff81-0c4b-459b-b2c5-0209f8b332b6 · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Countering Adversarial Images using Input Transformations

Reference 37

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Observation f7f21761-f5a9-47a9-9b9b-ed58934a2bbe · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Mitigating Adversarial Effects Through Randomization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.165088Z

Source-reported events for the cited work

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Observation 07d7ff29-99ff-4d3c-b992-caf0b4a68966 · outbound

This paper cites APE-GAN: Adversarial Perturbation Elimination with GAN.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition APE-GAN: Adversarial Perturbation Elimination with GAN

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:10:18.214437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 24b7af8f-9f5b-4318-a6f1-dc38dfd680cc · outbound

This paper cites A public dataset for fine-grained ship classification in optical remote sensing images.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition A public dataset for fine-grained ship classification in optical remote sensing images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.319415Z

Source-reported events for the cited work

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Observation 457b741b-7a17-46ac-9453-831fbd48a375 · outbound

This paper cites A benchmark data set for aircraft type recognition from remote sensing images.Applied Soft Computing, 89:106132, 2020.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition A benchmark data set for aircraft type recognition from remote sensing images.Applied Soft Computing, 89:106132, 2020

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.311898Z

Source-reported events for the cited work

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Observation 950a110c-0c7c-45cf-a126-a3b1b46eafad · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.174270Z

Source-reported events for the cited work

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Observation 9227c9ab-a8cb-4488-aa27-3a0462b227ee · outbound

This paper cites Deep residual learning for image recognition.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Deep residual learning for image recognition

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T22:10:18.176750Z

Source-reported events for the cited work

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Observation 31c59fac-05a4-44bf-9850-790aae931a1e · outbound

This paper cites Densely connected convolutional networks.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Densely connected convolutional networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.299166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbc1b439-79e5-4229-9664-0b26764b0bc3 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:10:18.290866Z

Source-reported events for the cited work

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Pith citing papers

Observation 34e1d5fd-aa3b-4969-814e-dd9b8b655214 · inbound

AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models cites this paper.

AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T04:34:31.228186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.