Paper Citation Record · LEDGER

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries

As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.23283.

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

pith.paper-citation-record.v1
2505.23283 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:52:51.209586Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:19:45.340339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:19:45.438795Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy36
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb225179-fd8c-4f39-a932-a439f138b97c · outbound

This paper cites Deep fake geography? when geospatial data encounter artificial intelligence.Cartography and Geographic Information Science, 48(4):338–352, 2021.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Deep fake geography? when geospatial data encounter artificial intelligence.Cartography and Geographic Information Science, 48(4):338–352, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:53:00.941404Z

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 bdc30e62-33f8-4b3e-bc0d-bd061c496045 · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:53:00.787298Z

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 889b9056-9804-4392-83e3-9d2ad20e8d0c · outbound

This paper cites DM-AER-DeepFake-V1 dataset, 2022.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries DM-AER-DeepFake-V1 dataset, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:53:00.571813Z

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 14a2b152-9cbf-4a39-852c-e6982390b9bd · outbound

This paper cites Fldcf: A collaborative framework for forgery localization and detection in satellite imagery.IEEE Transactions on Geo- science and Remote Sensing, 2024.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Fldcf: A collaborative framework for forgery localization and detection in satellite imagery.IEEE Transactions on Geo- science and Remote Sensing, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:53:00.382760Z

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 88536960-f93c-42e0-808a-007ed48b762b · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Infor- mation Processing Systems, 33:6840–6851, 2020.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Denoising diffusion probabilistic models.Advances in Neural Infor- mation Processing Systems, 33:6840–6851, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:53:00.224499Z

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 3fd3a348-6e64-4921-b8d9-a2f1684b05f0 · outbound

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

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries High-resolution image synthesis with latent diffusion models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:52:46.750305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f08dd22b-8a16-4ee3-89b2-01ab3c52e2d7 · outbound

This paper cites Av-deepfake1m: A large-scale llm-driven audio-visual deepfake dataset.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Av-deepfake1m: A large-scale llm-driven audio-visual deepfake dataset

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:53:00.051472Z

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 e96f4340-ef72-468c-abd8-117f594cecf6 · outbound

This paper cites Dire for diffusion-generated image detection.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Dire for diffusion-generated image detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:52:46.993288Z

Source-reported events for the cited work

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Observation b08e339a-11e3-4b58-8225-e6238265f83a · outbound

This paper cites Advanc- ing generalized deepfake detector with forgery perception guidance.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Advanc- ing generalized deepfake detector with forgery perception guidance

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:59.924377Z

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 67d0dc52-234a-44f2-862c-5b32da888986 · outbound

This paper cites Urban green space planning based on re- mote sensing and geographic information systems.Remote Sensing, 14(17):4213, 2022.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Urban green space planning based on re- mote sensing and geographic information systems.Remote Sensing, 14(17):4213, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:59.697828Z

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 0bfc3b76-3c92-4e38-a041-6bd1807ce820 · outbound

This paper cites Remote sensing big data for water envi-6 ronment monitoring: Current status, challenges, and future prospects.Earth’s Future, 10(2):e2021EF002289, 2022.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing big data for water envi-6 ronment monitoring: Current status, challenges, and future prospects.Earth’s Future, 10(2):e2021EF002289, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:59.460726Z

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 e3f48419-09a2-4250-a657-742849575b9c · outbound

This paper cites Remote sensing of irrigated agriculture: Oppor- tunities and challenges.Remote sensing, 2(9):2274–2304, 2010.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing of irrigated agriculture: Oppor- tunities and challenges.Remote sensing, 2(9):2274–2304, 2010

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:59.204729Z

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 c8c51755-0ca4-46b0-8211-64b13faed4e9 · outbound

This paper cites Hypersectral imaging for military and security applica- tions: Combining myriad processing and sensing tech- niques.IEEE Geoscience and Remote Sensing Magazine, 7(2):101–117, 2019.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Hypersectral imaging for military and security applica- tions: Combining myriad processing and sensing tech- niques.IEEE Geoscience and Remote Sensing Magazine, 7(2):101–117, 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:58.949271Z

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 d04bf71e-c9dc-4983-813f-c0a6900fe72a · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries A style-based generator architecture for generative adversarial networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:52:47.807914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c03e7d-0bab-4844-a35c-1b41c7734e4b · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:52:58.700325Z

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 230d8dec-77b5-4bca-bc89-d207a2eadc7b · outbound

This paper cites Analyzing and improv- ing the image quality of stylegan.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Analyzing and improv- ing the image quality of stylegan

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:58.548130Z

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 996f8942-c682-4a51-9555-ce99f817ae2f · outbound

This paper cites Diffusion models beat GANs on image synthesis.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Diffusion models beat GANs on image synthesis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:58.276262Z

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 5ae28784-bd38-46cd-b1db-154f0d057e2f · outbound

This paper cites Wildfake: A large-scale and hierarchical dataset for ai-generated images detection.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Wildfake: A large-scale and hierarchical dataset for ai-generated images detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:57.886610Z

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 65004087-ada3-4954-9721-cc27e781d8de · outbound

This paper cites Genimage: A million-scale bench- mark for detecting ai-generated image.Advances in Neural Information Processing Systems, 36:77771–77782, 2023.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Genimage: A million-scale bench- mark for detecting ai-generated image.Advances in Neural Information Processing Systems, 36:77771–77782, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:57.535302Z

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 2b36f9a6-519f-4a29-b6c2-05ea586d79a0 · outbound

This paper cites Artifact: A large-scale dataset with artificial and factual images for generalizable and robust synthetic im- age detection.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Artifact: A large-scale dataset with artificial and factual images for generalizable and robust synthetic im- age detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:57.235248Z

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 4c5c29fd-e543-4681-949b-511389f030d2 · outbound

This paper cites Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:56.930362Z

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 7785e362-0a79-49c8-a059-0dfb3393981c · outbound

This paper cites Holistically-nested edge detection.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Holistically-nested edge detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:56.653354Z

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 71a0eb0c-4812-4ffa-900c-633d60151036 · outbound

This paper cites A computational approach to edge detec- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, 1986.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries A computational approach to edge detec- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698, 1986

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:56.346695Z

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 882e2c44-5e3a-4a71-8666-e890e89bc802 · outbound

This paper cites Remote sensing image dataset expansion based on generative ad- versarial networks with modified shuffle attention.Sensors, 21(14), 2021.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing image dataset expansion based on generative ad- versarial networks with modified shuffle attention.Sensors, 21(14), 2021

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:56.064219Z

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 8cb06ceb-b9e9-4fbf-9ab9-438084627b1a · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:52:55.731694Z

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 68445c2b-ed01-42cf-98dc-223d3d65ef7f · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:52:55.481108Z

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 26bfe580-2107-4cfd-9c9d-a9cefb912b19 · outbound

This paper cites Text-to-remote-sensing-image generation with structured generative adversarial networks.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Text-to-remote-sensing-image generation with structured generative adversarial networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:55.173149Z

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 e4f6d9b0-00a4-4001-b6e1-7381e4f85c40 · outbound

This paper cites Remote sensing image synthesis via graphical generative adversarial networks.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing image synthesis via graphical generative adversarial networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:54.859232Z

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 c39ca481-75d5-4948-8bc6-672aab00593f · outbound

This paper cites Disastergan: Generative adversarial networks for remote sensing disaster image generation.Remote Sensing, 13 (21), 2021.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Disastergan: Generative adversarial networks for remote sensing disaster image generation.Remote Sensing, 13 (21), 2021

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:54.630422Z

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 5c201a3e-29ea-4187-8a61-fe894ef299a8 · outbound

This paper cites Remote sensing image synthesis via semantic embed- ding generative adversarial networks.IEEE Transactions on Geoscience and Remote Sensing, 61:1–11, 2023.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing image synthesis via semantic embed- ding generative adversarial networks.IEEE Transactions on Geoscience and Remote Sensing, 61:1–11, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:54.451337Z

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 82540e3f-7c3d-4f16-8bd6-21cdf6667044 · outbound

This paper cites Crs-diff: Controllable remote sensing image generation with diffusion model.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Crs-diff: Controllable remote sensing image generation with diffusion model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:54.284341Z

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 d532b1ee-c76d-44eb-b7c0-5ff5a450bae0 · outbound

This paper cites Geosynth: Contextually-aware high- resolution satellite image synthesis.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Geosynth: Contextually-aware high- resolution satellite image synthesis

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:54.021781Z

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 565e8611-5235-4931-ac1f-cfe4cd0ea2b2 · outbound

This paper cites Diffusionsat: A generative foundation model for satellite imagery.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Diffusionsat: A generative foundation model for satellite imagery

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:53.831382Z

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 0e1029cf-9d6b-4320-84aa-b3e440a268ae · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:52:53.589454Z

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 66a96db6-41fa-4f18-aa94-07d5b450a1a1 · outbound

This paper cites Tackling few-shot segmentation in re- mote sensing via inpainting diffusion model.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Tackling few-shot segmentation in re- mote sensing via inpainting diffusion model

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:53.428156Z

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 44840802-5849-4d60-b0e9-889dabb623a8 · outbound

This paper cites Efficient and controllable remote sensing fake sample generation based on diffusion model.IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023.7.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Efficient and controllable remote sensing fake sample generation based on diffusion model.IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023.7

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:53.183315Z

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 814004a6-770f-4a12-b9c0-ee3819da3582 · outbound

This paper cites Rs5m and georsclip: A large scale vision-language dataset and a large vision-language model for remote sens- ing.IEEE Transactions on Geoscience and Remote Sens- ing, 62:1–23, 2024.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Rs5m and georsclip: A large scale vision-language dataset and a large vision-language model for remote sens- ing.IEEE Transactions on Geoscience and Remote Sens- ing, 62:1–23, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:53.017898Z

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 2b05d288-4b00-4f69-8f40-2d088719c5a0 · outbound

This paper cites Remote sensing semantic segmentation quality assessment based on vision language model.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Remote sensing semantic segmentation quality assessment based on vision language model

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:52.810628Z

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 e0e0d987-15d7-482c-b025-bbd8aad889d0 · outbound

This paper cites Geochat: Grounded large vision-language model for remote sensing.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Geochat: Grounded large vision-language model for remote sensing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:52.612115Z

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 f8b83bcc-5281-40b0-8839-a3bbd7748292 · outbound

This paper cites Fast segment anything.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Fast segment anything

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:52.428767Z

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 81ecda44-8f68-4c86-a98e-005ec37cd6b4 · outbound

This paper cites Planet dump.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Planet dump

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:52.215729Z

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 6b0e69fa-e3b4-4a76-94f4-a7af9f7c3381 · outbound

This paper cites Functional map of the world.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Functional map of the world

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:52:50.734278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c7d3d31-6212-47a2-ab8e-e32231a73dc7 · outbound

This paper cites Towards universal fake image detectors that generalize across gener- ative models.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Towards universal fake image detectors that generalize across gener- ative models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:52.025317Z

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 e60a700b-1044-4dc9-a76d-7a8a17dd12d6 · outbound

This paper cites A sanity check for ai- generated image detection.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries A sanity check for ai- generated image detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:51.788025Z

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 c5aa6f80-302c-4398-9f8d-2d539ee7e90a · outbound

This paper cites Improving synthetic image detection towards generalization: An image transformation perspec- tive.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Improving synthetic image detection towards generalization: An image transformation perspec- tive

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:52:51.588339Z

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 d0819750-5df6-433f-bad4-6b40e63883c6 · outbound

This paper cites an unresolved cited work.

RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:52:51.394999Z

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.

Pith citing papers

Observation edd70de3-6391-41e9-8cf1-e059f370f8dd · inbound

Towards a satellite image manipulation and deepfake localization benchmark dataset cites this paper.

Towards a satellite image manipulation and deepfake localization benchmark dataset RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:19:45.442484Z

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.