SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection | Paper Citation Record

Observation df7750c9-29e7-474b-a569-3fdddb632e34 · outbound

This paper cites Art and the science of generative ai,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Art and the science of generative ai,

Reference 1

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This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Text-to-image Diffusion Models in Generative AI: A Survey

Reference 2

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This paper cites A survey on generative diffusion models,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection A survey on generative diffusion models,

Reference 3

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This paper cites an unresolved cited work.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Unresolved cited work

Reference 4

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Observation d7afd4de-c8f1-4dde-bd1b-9bf63f2d4e36 · outbound

This paper cites Deepfake: New era in the age of disinformation & end of reliable journalism,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deepfake: New era in the age of disinformation & end of reliable journalism,

Reference 5

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Observation 34fd9a6c-5e29-447a-8179-cf6c9a161441 · outbound

This paper cites Deep fake geography? when geospatial data encounter artificial intelligence,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deep fake geography? when geospatial data encounter artificial intelligence,

Reference 6

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Observation 42f90c1d-d85d-4abd-9226-508236d95131 · outbound

This paper cites Deepfake satellite imagery detection with multi-attention and super resolution,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deepfake satellite imagery detection with multi-attention and super resolution,

Reference 7

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Observation 7dadf047-7ced-494e-90d8-0f9b80565c6a · outbound

This paper cites Bringing satellites down to earth: Six steps to more ethical remote sensing,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Bringing satellites down to earth: Six steps to more ethical remote sensing,

Reference 8

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Observation d9d36197-4e8c-4ca7-b250-11d659438a21 · outbound

This paper cites Image Fusion in Remote Sensing: An Overview and Meta Analysis.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Image Fusion in Remote Sensing: An Overview and Meta Analysis

Reference 9

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Observation 9e2fc1c0-aca1-4d5d-812d-7e96da9921c2 · outbound

This paper cites Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier

Reference 10

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Observation 87cba02e-03b5-4eb3-ba83-7bdb37d61b3d · outbound

This paper cites On deep learning approach in remote sensing data forgery detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection On deep learning approach in remote sensing data forgery detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.505180Z

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Observation f20cce4b-d707-42b8-a8b7-a7eefa46c02f · outbound

This paper cites Spatial-spectral middle cross-attention fusion network for hyperspectral image superresolution,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Spatial-spectral middle cross-attention fusion network for hyperspectral image superresolution,

Reference 12

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Observation 67d17d2e-f8d9-4457-8982-2a06823a7683 · outbound

This paper cites Combined model color- correction method utilizing external low-frequency reference signals for large-scale optical satellite image mosaics,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Combined model color- correction method utilizing external low-frequency reference signals for large-scale optical satellite image mosaics,

Reference 13

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Observation edd44649-fb99-4b8a-8fe9-3fc4a2514880 · outbound

This paper cites Protecting world leaders against deep fakes,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Protecting world leaders against deep fakes,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.461114Z

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Observation 45a05b20-c112-4a7d-8fce-0d11eeda6a35 · outbound

This paper cites Exposing deep fakes using inconsistent head poses,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Exposing deep fakes using inconsistent head poses,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.446657Z

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Observation 9400930b-156b-47c5-9c9c-344ebbff40e2 · outbound

This paper cites A review of deep learning- based approaches for deepfake content detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection A review of deep learning- based approaches for deepfake content detection,

Reference 16

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Observation cd7a6bf2-62cd-4a38-852d-6281ab2b7998 · outbound

This paper cites Deep feature extraction for face liveness detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deep feature extraction for face liveness detection,

Reference 17

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Observation 8704af90-e9f1-4f6c-b0e6-062939ff1bb6 · outbound

This paper cites Fake faces identification via convolutional neural network,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Fake faces identification via convolutional neural network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.402607Z

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Observation 51daeadf-4864-43ec-833c-a065ac5390d4 · outbound

This paper cites Exposing deepfake videos by detecting face warping artifacts,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Exposing deepfake videos by detecting face warping artifacts,

Reference 19

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Observation b90168ec-0a12-412b-9d1e-dbeef745b849 · outbound

This paper cites Investigation of comparison on modified cnn techniques to classify fake face in deepfake videos,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Investigation of comparison on modified cnn techniques to classify fake face in deepfake videos,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.372589Z

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Observation 480c9d11-f942-43e0-8cc3-ad02f8720a94 · outbound

This paper cites Generalizing face forgery detection with high-frequency features,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Generalizing face forgery detection with high-frequency features,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.359412Z

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Observation d46caac8-301b-4d57-928e-ad28b097f9b9 · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Progressive growing of gans for improved quality, stability, and variation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.346279Z

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Observation 4fa4188d-36b9-4eb8-9eb2-1c173eeb667c · outbound

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection A style-based generator architecture for generative adversarial networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.331246Z

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Observation 00215f06-92c8-4c46-8af3-5a0d1b85e365 · outbound

This paper cites Face2face: Real-time face capture and reenactment of rgb videos,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Face2face: Real-time face capture and reenactment of rgb videos,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.316763Z

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Observation 32483f47-dfb6-48bc-a95b-2ead958df7e6 · outbound

This paper cites DeepFaceLab: Integrated, flexible and extensible face-swapping framework.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection DeepFaceLab: Integrated, flexible and extensible face-swapping framework

Reference 25

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Observation 18c73db5-ed28-459b-8381-920ebdfb3a6d · outbound

This paper cites Thinking in frequency: Face forgery detection by mining frequency-aware clues,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Thinking in frequency: Face forgery detection by mining frequency-aware clues,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.302746Z

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Observation c00f689e-e53c-4b8e-9929-51e2df7ffa71 · outbound

This paper cites Fcanet: Frequency channel attention networks,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Fcanet: Frequency channel attention networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.288841Z

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Observation 85273a66-e0c6-48d0-99cb-65b434196635 · outbound

This paper cites Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.274008Z

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Observation 9947b332-e044-465a-a8d4-7d82d3025008 · outbound

This paper cites Frequency-aware attentional feature fusion for deepfake detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Frequency-aware attentional feature fusion for deepfake detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.258514Z

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Observation d4e913b6-1ddd-45d3-914b-e8e5862da254 · outbound

This paper cites Denoising diffusion probabilistic models,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Denoising diffusion probabilistic models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.244373Z

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Observation 39c69250-0419-404d-a85b-8cd0bd5c43d8 · outbound

This paper cites Zero-shot text-to-image generation,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Zero-shot text-to-image generation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.231441Z

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Observation 1f3d722b-39b2-4729-9759-e41c195e8112 · outbound

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection High- resolution image synthesis with latent diffusion models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.217969Z

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Observation bd419b13-d180-4856-ad2b-39e67838a03b · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 33

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Observation 86b2eec3-77ad-4a9a-8881-aac8f6aabad5 · outbound

This paper cites Visualizing data using t-sne.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Visualizing data using t-sne

Reference 34

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Imagenet classification with deep convolutional neural networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.180375Z

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Observation 8211a666-b2bd-42f8-8596-e368bcea45bc · outbound

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Very deep convolutional networks for large-scale image recognition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.167282Z

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Observation 40334c85-b172-43ed-9a77-602b43219815 · outbound

This paper cites Fake face detection methods: Can they be generalized?.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Fake face detection methods: Can they be generalized?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.154220Z

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Observation e7017803-86fc-4a6b-bda6-b714c72577a8 · outbound

This paper cites What makes fake images detectable? Understanding properties that generalize,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection What makes fake images detectable? Understanding properties that generalize,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.139856Z

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Observation 369d78dc-4035-462d-b998-1cd53a9d0a94 · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Xception: Deep learning with depthwise separable convolu- tions,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.126322Z

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Observation 00d24e77-0215-4c20-bcac-735ec1442a96 · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Aggregated residual transformations for deep neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.113323Z

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Observation 62a42389-165c-425e-a50f-009139c572a9 · outbound

This paper cites Deepfake Video Detection Using Convolutional Vision Transformer.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deepfake Video Detection Using Convolutional Vision Transformer

Reference 41

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This paper cites Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.100091Z

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Observation b8c17d56-fcc4-4301-97bd-7cd073138352 · outbound

This paper cites Generative AI in Vision: A Survey on Models, Metrics and Applications.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Generative AI in Vision: A Survey on Models, Metrics and Applications

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:50:06.529423Z

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Observation 86b1d3e9-d158-4cf1-a081-61d030034489 · outbound

This paper cites Robustness of copy-move forgery detection under high jpeg compression artifacts,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Robustness of copy-move forgery detection under high jpeg compression artifacts,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.086827Z

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Observation fbddc634-c79e-422f-bf5a-05b8e1599a18 · outbound

This paper cites Detecting and simulating artifacts in gan fake images,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Detecting and simulating artifacts in gan fake images,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.072901Z

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Observation 585da72c-42c5-472c-a8c4-42b48e66bdc8 · outbound

This paper cites Leveraging frequency analysis for deep fake image recognition,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Leveraging frequency analysis for deep fake image recognition,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.059578Z

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Observation 17963136-171f-4d24-81f8-80a0bb9d3222 · outbound

This paper cites an unresolved cited work.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:50:07.046121Z

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Observation 6f199f6f-7bee-4f15-b992-d3bd9f306e86 · outbound

This paper cites Bihpf: Bilateral high-pass filters for robust deepfake detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Bihpf: Bilateral high-pass filters for robust deepfake detection,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.032346Z

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Observation d2748cce-5941-4eda-9ce3-c0a1b7252d66 · outbound

This paper cites Inconsistency-aware wavelet dual-branch network for face forgery detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Inconsistency-aware wavelet dual-branch network for face forgery detection,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.018784Z

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Observation 747af645-badb-4c3d-a163-22172051334d · outbound

This paper cites Frequency spectrum with multi-head attention for face forgery detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Frequency spectrum with multi-head attention for face forgery detection,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:07.005311Z

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Observation 434cb388-333f-4ed4-af2b-7dbdc8cbf0de · outbound

This paper cites Add: Frequency attention and multi-view based knowledge distillation to detect low-quality compressed deepfake images,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Add: Frequency attention and multi-view based knowledge distillation to detect low-quality compressed deepfake images,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.990975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88329495-35be-4eb8-8318-d0bbf3d60b64 · outbound

This paper cites Frepgan: Robust deepfake detection using frequency-level perturbations,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Frepgan: Robust deepfake detection using frequency-level perturbations,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.976130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4c2b5f2-799c-4594-bcaf-2260078a1191 · outbound

This paper cites Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.962075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60c3313c-ce3a-4751-840d-8ab1d8e38736 · outbound

This paper cites Local relation learning for face forgery detection,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Local relation learning for face forgery detection,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.947312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d54625b-2345-41cd-893c-19009470bfbf · outbound

This paper cites Joint learning of frequency and spatial domains for dense image prediction,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Joint learning of frequency and spatial domains for dense image prediction,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.932774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 491e80cd-3cde-4e24-a829-9eba90f57234 · outbound

This paper cites Remote sensing image forgery detection using modified u-net,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Remote sensing image forgery detection using modified u-net,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.916199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d13e7087-02d9-490b-bf52-96ccdd9a584e · outbound

This paper cites Geo-DefakeHop: High-Performance Geographic Fake Image Detection.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Geo-DefakeHop: High-Performance Geographic Fake Image Detection

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:50:06.504504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 762c8835-324d-44f1-b8e4-4c0dbc5cea44 · outbound

This paper cites Deep residual learning for image recognition,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Deep residual learning for image recognition,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.348807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca0d394c-064d-4afd-beaa-2a004f4238ec · outbound

This paper cites Cbam: Convolutional block attention module,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Cbam: Convolutional block attention module,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.889943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0a0cc35a-ea56-4a78-9ed6-e94b832a42c9 · outbound

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.358644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5752c599-1227-44ce-8890-5e7eb002907b · outbound

This paper cites Rethinking the inception architecture for computer vision,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Rethinking the inception architecture for computer vision,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.865427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d85521f1-3608-4762-8a89-b1d20d70c40d · outbound

This paper cites Conformer: Local features coupling global representations for visual recognition,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Conformer: Local features coupling global representations for visual recognition,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.847961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation edb8c12d-df45-42cb-a098-8706c0f29ef3 · outbound

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

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Imagenet: A large-scale hierarchical image database,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.822958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fae6fa38-6219-45a6-a7a9-4e6c29bef68a · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection A simple framework for contrastive learning of visual representations,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.800636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c2285c37-c532-4dd1-8abf-0c296a6a4b5b · outbound

This paper cites An empirical study of remote sensing pretraining,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection An empirical study of remote sensing pretraining,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.769813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f1a48c9b-5c0e-42bf-aee6-68d03a3d74c9 · outbound

This paper cites Tov: The original vision model for optical remote sensing image understanding via self- supervised learning,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Tov: The original vision model for optical remote sensing image understanding via self- supervised learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.737599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 808ddf78-6dde-4ffe-b6df-14928e6084e7 · outbound

This paper cites BAM: Bottleneck Attention Module.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection BAM: Bottleneck Attention Module

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.389475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffc43458-e342-4614-a6eb-809803640194 · outbound

This paper cites Squeeze-and-excitation networks,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Squeeze-and-excitation networks,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.394655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41ed4208-0dcc-438a-a61a-be13442215da · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Eca-net: Efficient channel attention for deep convolutional neural networks,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.399173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4db62129-d6af-4008-a85f-ac518e45e542 · outbound

This paper cites The isprs benchmark on urban object classification and 3d building reconstruction,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection The isprs benchmark on urban object classification and 3d building reconstruction,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.694935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d087c1fc-59dc-40e5-b74c-5954c800c8f4 · outbound

This paper cites Visual instruction tuning,.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection Visual instruction tuning,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.674243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e0de8095-6b5e-4156-a81e-d353d6f4f0ef · outbound

This paper cites From Text to Pixel: Advancing Long-Context Understanding in MLLMs.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection From Text to Pixel: Advancing Long-Context Understanding in MLLMs

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:06.414052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b07ed20-51d9-42cb-b296-ad32e8bd1ec7 · outbound

This paper cites His research interests include ecological remote sensing, deep learning for extracting remote Sensing information, vegetation phenology and ice phenology.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection His research interests include ecological remote sensing, deep learning for extracting remote Sensing information, vegetation phenology and ice phenology

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.630773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d52fa467-29e8-4e85-a57e-ff86b5eddd85 · outbound

This paper cites His research interests include computer vision, continual learning, and remote sensing image processing.

SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection His research interests include computer vision, continual learning, and remote sensing image processing

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:06.649565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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