Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:52:51.209586Z
Paper Citation Record · LEDGER
RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries
As of 7 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:19:45.340339Z
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
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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
Unavailable: canonical work link unavailable.
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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.