Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence | Paper Citation Record

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This paper cites Recent advances in techniques for hyperspectral image processing,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Recent advances in techniques for hyperspectral image processing,

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Unresolved cited work

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This paper cites Synergies between vswir and tir data for the urban environment: An evaluation of the potential for the hyperspectral infrared imager (hyspiri) decadal survey mission,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Synergies between vswir and tir data for the urban environment: An evaluation of the potential for the hyperspectral infrared imager (hyspiri) decadal survey mission,

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This paper cites Recent advances of hyperspectral imaging technology and applications in agriculture,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Recent advances of hyperspectral imaging technology and applications in agriculture,

Reference 4

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This paper cites Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques,

Reference 5

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Observation 061cbce8-69ed-4ed5-9b3f-a965ed36b39b · outbound

This paper cites Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning ap- proach,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning ap- proach,

Reference 6

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This paper cites Convolutional neural networks for hyperspec- tral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Convolutional neural networks for hyperspec- tral image classification,

Reference 7

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Observation d8ecb5fd-9359-44e1-ab9d-696fb0606ecf · outbound

This paper cites Deep learning for hyperspectral image classification: An overview,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Deep learning for hyperspectral image classification: An overview,

Reference 8

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This paper cites Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification,

Reference 9

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Observation 0e8f0a19-10c5-4b40-adff-9a7355c0fd88 · outbound

This paper cites Dynamic super-pixel nor- malization for robust hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Dynamic super-pixel nor- malization for robust hyperspectral image classification,

Reference 10

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Observation 79014590-4910-4436-8f48-4dba1b8b903c · outbound

This paper cites Dessa-net model: Hyperspectral image classification using an entropy filter with spatial and spectral attention modules on deepnet,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Dessa-net model: Hyperspectral image classification using an entropy filter with spatial and spectral attention modules on deepnet,

Reference 11

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Observation 9f8724e1-da72-4d1b-a9f7-e53fe04107e4 · outbound

This paper cites Atsfcnn: a novel attention-based triple-stream fused cnn model for hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Atsfcnn: a novel attention-based triple-stream fused cnn model for hyperspectral image classification,

Reference 12

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This paper cites Graph information aggregation cross-domain few-shot learning for hyperspec- tral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Graph information aggregation cross-domain few-shot learning for hyperspec- tral image classification,

Reference 13

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This paper cites Deep cross- domain few-shot learning for hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Deep cross- domain few-shot learning for hyperspectral image classification,

Reference 14

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Observation c42db61f-78f4-4bec-9ff2-70d0e1a60f75 · outbound

This paper cites Refined prototypical contrastive learning for few-shot hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Refined prototypical contrastive learning for few-shot hyperspectral image classification,

Reference 15

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This paper cites Intriguing properties of neural networks,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Intriguing properties of neural networks,

Reference 16

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This paper cites Adversarial examples in the physical world.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Adversarial examples in the physical world

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Observation e47504a3-30e4-40a3-b601-b81c76d079b3 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Explaining and harnessing adversarial examples,

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

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Towards deep learning models resistant to adversarial attacks,

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This paper cites Boosting adversarial attacks with momentum,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Boosting adversarial attacks with momentum,

Reference 20

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This paper cites Black-box adversarial at- tacks with limited queries and information,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Black-box adversarial at- tacks with limited queries and information,

Reference 21

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Curls & whey: Boosting black-box adversarial attacks,

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This paper cites Delving into transferable adver- sarial examples and black-box attacks.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Delving into transferable adver- sarial examples and black-box attacks

Reference 23

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This paper cites Transferable adversarial perturbations,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Transferable adversarial perturbations,

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This paper cites Nesterov accelerated gradient and scale invariance for adversarial attacks,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Nesterov accelerated gradient and scale invariance for adversarial attacks,

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This paper cites Improving transferability of adversarial examples with input diversity,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Improving transferability of adversarial examples with input diversity,

Reference 26

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

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Admix: Enhancing the transferability of adversarial attacks,

Reference 27

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This paper cites How transferable are features in deep neural networks?.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence How transferable are features in deep neural networks?

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This paper cites Fda: Feature disruptive attack,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Fda: Feature disruptive attack,

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This paper cites Feature space perturba- tions yield more transferable adversarial examples,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Feature space perturba- tions yield more transferable adversarial examples,

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This paper cites Universal adversarial examples in remote sens- ing: Methodology and benchmark,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Universal adversarial examples in remote sens- ing: Methodology and benchmark,

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This paper cites Universal object- level adversarial attack in hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Universal object- level adversarial attack in hyperspectral image classification,

Reference 32

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Boosting transferability of targeted adversarial examples with non-robust feature alignment,

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This paper cites Generating adversarial examples against remote sensing scene classification via feature approxi- mation,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Generating adversarial examples against remote sensing scene classification via feature approxi- mation,

Reference 34

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This paper cites Enhancing the transferability of adversarial attacks through variance tuning,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Enhancing the transferability of adversarial attacks through variance tuning,

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Evading defenses to trans- ferable adversarial examples by translation-invariant attacks,

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Observation 698f3978-0003-495d-8f61-5b1fc1e8c505 · outbound

This paper cites Robust superpixel-guided attentional adversarial attack,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Robust superpixel-guided attentional adversarial attack,

Reference 37

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Frequency domain model augmentation for adversarial attack,

Reference 38

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This paper cites Mixcam-attack: Boosting the transferability of adversarial examples with targeted data augmentation,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Mixcam-attack: Boosting the transferability of adversarial examples with targeted data augmentation,

Reference 39

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Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Boosting adversarial trans- ferability by block shuffle and rotation,

Reference 40

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This paper cites Boosting the transferability of adversarial examples via local mixup and adaptive step size,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Boosting the transferability of adversarial examples via local mixup and adaptive step size,

Reference 41

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Observation 16f9c8df-de5f-4540-918f-15638e87d35c · outbound

This paper cites Boosting the transferabil- ity of adversarial samples via attention,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Boosting the transferabil- ity of adversarial samples via attention,

Reference 42

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Observation 1ab72980-33e2-470a-b7b7-1a23f958e62e · outbound

This paper cites Improving adversarial transfer- ability via intermediate-level perturbation decay,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Improving adversarial transfer- ability via intermediate-level perturbation decay,

Reference 43

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This paper cites Diversifying the high-level features for better adversarial transferability,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Diversifying the high-level features for better adversarial transferability,

Reference 44

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This paper cites Adversarial Example in Remote Sensing Image Recognition.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Adversarial Example in Remote Sensing Image Recognition

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This paper cites Assessing the threat of adversarial ex- amples on deep neural networks for remote sensing scene classification: Attacks and defenses,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Assessing the threat of adversarial ex- amples on deep neural networks for remote sensing scene classification: Attacks and defenses,

Reference 46

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Observation bf2184a5-3028-4ac7-8023-9a49be34dfb2 · outbound

This paper cites An empirical study of adversarial examples on remote sensing image scene classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence An empirical study of adversarial examples on remote sensing image scene classification,

Reference 47

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Observation b807ce13-60e6-48fe-9bbd-9db9ca9b0c23 · outbound

This paper cites Generating natural adversarial remote sensing images,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Generating natural adversarial remote sensing images,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:01.648493Z

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Observation b321b4f3-e069-42ab-a5e4-097d9dea94a4 · outbound

This paper cites Generating imperceptible and cross-resolution remote sensing adversarial examples based on implicit neural representations,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Generating imperceptible and cross-resolution remote sensing adversarial examples based on implicit neural representations,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:01.503937Z

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Observation bee83649-93cf-4bc2-9983-88e22e9e1e9f · outbound

This paper cites Distillation-based cross-model transfer- able adversarial attack for remote sensing image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Distillation-based cross-model transfer- able adversarial attack for remote sensing image classification,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:01.292905Z

Source-reported events for the cited work

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Observation ab950c93-6f3f-45c0-8152-c2849decf670 · outbound

This paper cites Iopa-fracat: Research on improved one-pixel adversarial attack and fractional defense in hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Iopa-fracat: Research on improved one-pixel adversarial attack and fractional defense in hyperspectral image classification,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:01.206257Z

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 d606d31d-e2d8-4a41-a0c5-fb3d4afd6712 · outbound

This paper cites Attack-invariant attention feature for adversarial defense in hyperspectral image classifi- cation,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Attack-invariant attention feature for adversarial defense in hyperspectral image classifi- cation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:01.024767Z

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 fcc993ad-be7f-4343-8e65-624bc5590b74 · outbound

This paper cites Self-attention context network: Addressing the threat of adversarial attacks for hyperspectral image classification.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Self-attention context network: Addressing the threat of adversarial attacks for hyperspectral image classification

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:00.851589Z

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 7d257514-dd79-4c6b-a22f-16bcd342d2d6 · outbound

This paper cites S³anet: Spatial–spectral self-attention learning network for defending against adversarial attacks in hyperspectral image classification,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence S³anet: Spatial–spectral self-attention learning network for defending against adversarial attacks in hyperspectral image classification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:00.673634Z

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 3277a078-fa1c-4509-9f64-8ef3881cb0f8 · outbound

This paper cites Wfss: weighted fusion of spectral transformer and spatial self-attention for robust hyperspectral image classification against adversarial attacks,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Wfss: weighted fusion of spectral transformer and spatial self-attention for robust hyperspectral image classification against adversarial attacks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:00.538109Z

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 275f1301-9737-4b86-9e7d-f3a95a67a532 · outbound

This paper cites Masked spatial–spectral autoencoders are excellent hyperspectral defenders,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Masked spatial–spectral autoencoders are excellent hyperspectral defenders,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:00.391634Z

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 a2cbe3f3-21bd-49a1-ac66-26aa9a7f2c6f · outbound

This paper cites Apnet: A novel antiperturbation network for robust hyperspectral image classification against adversarial attacks,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Apnet: A novel antiperturbation network for robust hyperspectral image classification against adversarial attacks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:00.141502Z

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 7edb7c19-5e6a-40d5-93ff-388c40643921 · outbound

This paper cites Structure invariant transformation for better adversarial transferability,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Structure invariant transformation for better adversarial transferability,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:31:59.978675Z

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 2818e892-1036-4826-a526-69e72170a3d3 · outbound

This paper cites Transferable adversarial attacks for object detection using object-aware significant feature distortion,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Transferable adversarial attacks for object detection using object-aware significant feature distortion,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:31:59.798261Z

Source-reported events for the cited work

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Observation 5d259f32-835a-4b8e-9dee-0fd33277892d · outbound

This paper cites Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability,.

Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:31:59.592519Z

Source-reported events for the cited work

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