Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation | Paper Citation Record

Observation 7c32d03a-dcb3-4199-84c1-b1dda30be18a · outbound

This paper cites Hidemia: Hidden wavelet mining for privacy- enhancing medical image analysis,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Hidemia: Hidden wavelet mining for privacy- enhancing medical image analysis,

Reference 1

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Observation f0685e00-a265-4c72-a36a-57cda659b813 · outbound

This paper cites Purify unlearnable examples via rate-constrained variational autoencoders,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Purify unlearnable examples via rate-constrained variational autoencoders,

Reference 2

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Observation 22e3e94f-b0c1-40d1-8fe6-894bf47f7f2b · outbound

This paper cites Semantic deep hiding for robust unlearnable examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Semantic deep hiding for robust unlearnable examples,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.332743Z

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Observation 6c7a20d8-6ca0-4526-b5db-fcdc7450de8d · outbound

This paper cites Towards physical world backdoor attacks against skeleton action recognition,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Towards physical world backdoor attacks against skeleton action recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.324417Z

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Observation 593bf82c-2a92-4de1-8155-4e943ae5da00 · outbound

This paper cites Robust and transferable backdoor attacks against deep image compression with selective frequency prior,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Robust and transferable backdoor attacks against deep image compression with selective frequency prior,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.315850Z

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Observation e3c05785-3c8c-4606-9aae-aee14e03b424 · outbound

This paper cites Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems

Reference 6

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Observation 21527e1e-f475-438a-8767-2db01d6862f0 · outbound

This paper cites Backdoor attacks against no-reference image quality assessment models via a scalable trigger,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Backdoor attacks against no-reference image quality assessment models via a scalable trigger,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.306466Z

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Observation 3d095904-82c6-4ec7-add8-1996a26f1031 · outbound

This paper cites Intriguing properties of neural networks.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Intriguing properties of neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.162373Z

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Observation 2ac2595a-5000-44ac-8aa0-1ad53777bdc6 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Explaining and harnessing adversarial examples,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.298277Z

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Observation 9cf5afd2-a2b8-4bf9-a0e2-58f4238901f1 · outbound

This paper cites Mitigating the curse of dimensionality for certified robustness via dual randomized smoothing,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Mitigating the curse of dimensionality for certified robustness via dual randomized smoothing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.289568Z

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Observation 8afbd7f2-9d7a-4918-aa9f-eaa19ba8991d · outbound

This paper cites Transferable adversarial attacks on sam and its downstream models,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Transferable adversarial attacks on sam and its downstream models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.281209Z

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Observation 9d2a6459-0b64-4e0a-982d-66ca6aa63184 · outbound

This paper cites Adversarial attack vulnerability of medical image analysis systems: Unexplored factors,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Adversarial attack vulnerability of medical image analysis systems: Unexplored factors,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.271900Z

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Observation 5df6bdbf-855a-4707-822b-09debc1c3e46 · outbound

This paper cites Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.179577Z

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Observation b3dae4af-40bf-416e-aa3c-3d5fb5c2e42d · outbound

This paper cites Evaluating adversarial evasion attacks in the context of wireless communications,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Evaluating adversarial evasion attacks in the context of wireless communications,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.262817Z

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Observation 552dc0c8-cbde-420e-8a3d-a9d90a500613 · outbound

This paper cites Interpretable learning for self-driving cars by visualizing causal attention,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Interpretable learning for self-driving cars by visualizing causal attention,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.253274Z

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Observation 96f2b861-1c50-430c-a9d2-fb1ee997c0af · outbound

This paper cites Towards robust rain removal against adversarial attacks: A comprehensive benchmark analysis and beyond,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Towards robust rain removal against adversarial attacks: A comprehensive benchmark analysis and beyond,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.244137Z

Source-reported events for the cited work

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Observation 8d2d3ad8-048c-4c7b-93f5-1d0c5f077364 · outbound

This paper cites Benchmarking adversarial robustness of image shadow removal with shadow-adaptive attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Benchmarking adversarial robustness of image shadow removal with shadow-adaptive attacks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.235063Z

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Observation e50f30cd-de36-48d5-a813-c0feaf70d7f8 · outbound

This paper cites Backdoor attacks against deep image compression via adaptive frequency trigger,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Backdoor attacks against deep image compression via adaptive frequency trigger,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.226078Z

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Observation f2dedbc4-7d96-4f77-9f5f-9925ae2af892 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Black-box adversarial attacks with limited queries and information,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.216807Z

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Observation 122bb0a2-aaf2-4968-bc3d-8feb7c8aec61 · outbound

This paper cites Simple black-box adversarial attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Simple black-box adversarial attacks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.208004Z

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Observation f9ecd215-7f58-4035-aa21-725fc78d28d2 · outbound

This paper cites Improving black- box adversarial attacks with a transfer-based prior,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Improving black- box adversarial attacks with a transfer-based prior,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.199203Z

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Observation 42ffcd70-e568-4236-b5e6-7053069365f1 · outbound

This paper cites Coreset learning-based sparse black-box adversarial attack for video recognition,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Coreset learning-based sparse black-box adversarial attack for video recognition,

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T11:52:01.688885Z

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Observation 5d9effb0-5580-443c-9c54-5651e6db442c · outbound

This paper cites Query-efficient decision-based black-box patch attack,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Query-efficient decision-based black-box patch attack,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.215434Z

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Observation 2a54efc7-e266-4a77-9594-9ffe24c02c2c · outbound

This paper cites Quantization aware attack: Enhancing transferable adversarial attacks by model quantization,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Quantization aware attack: Enhancing transferable adversarial attacks by model quantization,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.218351Z

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Observation 110f8b43-9792-4a9b-905a-a92f48290ab9 · outbound

This paper cites Logit margin matters: Improving transferable targeted adversarial attack by logit calibration,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Logit margin matters: Improving transferable targeted adversarial attack by logit calibration,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.221268Z

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Observation da193967-a920-4a86-b882-98241f5574fa · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 26

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Observation e496e18e-24bd-4004-b9f4-5403acc585b3 · outbound

This paper cites Improving the transferability of adversarial samples by path-augmented method,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Improving the transferability of adversarial samples by path-augmented method,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.189848Z

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Observation 9b4fbda6-fc78-4334-bde2-01573ee32bae · outbound

This paper cites Transferable adversarial attacks on vision transformers with token gradient regularization,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Transferable adversarial attacks on vision transformers with token gradient regularization,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.180599Z

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Observation e1b157b7-3543-4f69-8e79-fcb4277f34ed · outbound

This paper cites Delving into transferable ad- versarial examples and black-box attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Delving into transferable ad- versarial examples and black-box attacks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.171308Z

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Observation c245fde6-e686-432c-8d23-43cdb6e51f85 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Practical black-box attacks against machine learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.237039Z

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Observation f9b9cb13-f141-47f8-9eb9-36939daac471 · outbound

This paper cites Ensemble adversarial training: Attacks and defenses,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Ensemble adversarial training: Attacks and defenses,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.154898Z

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Observation 0ada615f-00a2-42d2-b266-e4a1cbbcf1a1 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Towards deep learning models resistant to adversarial attacks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.145983Z

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Observation 70e55f5a-8541-4922-8582-4e2b70dd12c2 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Ima- genet: A large-scale hierarchical image database,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.136950Z

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Observation 818bbd92-20df-43b7-a227-686c65f6bb7d · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Theoretically principled trade-off between robustness and accuracy,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.128015Z

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Observation 7e2aeed3-5080-476c-8937-d10eee11fd52 · outbound

This paper cites Deep residual learning for image recognition,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Deep residual learning for image recognition,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.118786Z

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Observation d74d4f9e-a9f8-4c44-9942-a0d7e09f4288 · outbound

This paper cites Countering adver- sarial images using input transformations,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Countering adver- sarial images using input transformations,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.109934Z

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Observation fe22caaf-9455-456a-9356-1bf3290bc366 · outbound

This paper cites Deflecting adversarial attacks with pixel deflection,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Deflecting adversarial attacks with pixel deflection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.100618Z

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Observation fedd0a8e-1c3a-49b5-9a03-1467a6ad9b91 · outbound

This paper cites Pixelde- fend: Leveraging generative models to understand and defend against adversarial examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Pixelde- fend: Leveraging generative models to understand and defend against adversarial examples,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.090563Z

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Observation 7e165738-fcce-4279-9014-4f2ec270585d · outbound

This paper cites Defense against adversarial attacks using high-level representation guided denoiser,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Defense against adversarial attacks using high-level representation guided denoiser,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.080902Z

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Observation 3c08fd58-4c23-4073-bc83-9238045ae233 · outbound

This paper cites Comdefend: An efficient image compression model to defend adversarial examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Comdefend: An efficient image compression model to defend adversarial examples,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.071012Z

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Observation f1f7ac06-6164-4833-94ae-1f79a378e828 · outbound

This paper cites Diffusion models for adversarial purification,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Diffusion models for adversarial purification,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.061113Z

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Observation 583fdd90-0a08-4079-8aba-033a8160c0fe · outbound

This paper cites Synthesizing robust adversarial examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Synthesizing robust adversarial examples,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.051794Z

Source-reported events for the cited work

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Observation a3ecde08-d4dc-4089-abe3-7543818f9a6a · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

Reference 43

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Observation 136bfdbb-fab7-4f48-9aec-f16ba94044dd · outbound

This paper cites Transferable Adversarial Attack based on Integrated Gradients.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Transferable Adversarial Attack based on Integrated Gradients

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.286018Z

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Observation 6f95b162-f1d3-4635-a944-08c6d03cf686 · outbound

This paper cites Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.289289Z

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Observation 2870c608-4f54-4ee2-8934-3aa547df7ec7 · outbound

This paper cites Decision-based black-box attack against vision transformers via patch-wise adversarial removal,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Decision-based black-box attack against vision transformers via patch-wise adversarial removal,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.035860Z

Source-reported events for the cited work

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Observation 3cc4bf50-4687-4300-89dd-9ea88ab50c68 · outbound

This paper cites Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.026657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6fc9336e-4f94-48db-88cd-fc4605ad6fdf · outbound

This paper cites Adversarial examples in the physical world,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Adversarial examples in the physical world,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.299447Z

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Observation 59d63bce-5173-49b6-94f2-1cba76a4c0b0 · outbound

This paper cites Boosting adversarial attacks with momentum,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Boosting adversarial attacks with momentum,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:02.009454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ab93f839-f3c2-45d6-8baf-a4f939131b47 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.306099Z

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Observation 389e377a-f5d4-4bac-8f6e-908fbd394de8 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Enhancing the transferability of adversarial attacks through variance tuning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.999660Z

Source-reported events for the cited work

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Observation 7b6499d7-eb98-4062-8117-2ef356890cbe · outbound

This paper cites Boosting adversar- ial transferability via gradient relevance attack,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Boosting adversar- ial transferability via gradient relevance attack,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.990467Z

Source-reported events for the cited work

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Observation 415f8366-b95b-4d6f-b63d-1e2870c248d2 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Improving transferability of adversarial examples with input diversity,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.980698Z

Source-reported events for the cited work

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Observation 201f76bf-5e53-4a23-afa9-db3b5b02da4a · outbound

This paper cites Admix: Enhancing the trans- ferability of adversarial attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Admix: Enhancing the trans- ferability of adversarial attacks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.970645Z

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Observation 352aebe3-e19a-4f8c-9683-1e101fa0482a · outbound

This paper cites Boosting Adversarial Transferability by Block Shuffle and Rotation,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Boosting Adversarial Transferability by Block Shuffle and Rotation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.961762Z

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Observation a19fbda3-8349-43ee-a5d4-c650553e7144 · outbound

This paper cites Learning to transform dynamically for better adversarial transferability,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Learning to transform dynamically for better adversarial transferability,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.952837Z

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Observation 3432be2e-6da7-4dc2-b99c-534a915be15a · outbound

This paper cites Fda: Feature disruptive attack,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Fda: Feature disruptive attack,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.942397Z

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Observation 29bcbad2-8a68-40f8-8dd6-f75feb02c4dd · outbound

This paper cites Feature importance-aware transferable adversarial attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Feature importance-aware transferable adversarial attacks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.933718Z

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Observation f748d9da-4862-40d9-9268-09f973049e63 · outbound

This paper cites Improving adversarial transferability via neuron attribution- based attacks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Improving adversarial transferability via neuron attribution- based attacks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.923792Z

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Observation b190e87b-3a35-45bf-a74b-2cc424c761af · outbound

This paper cites Enhancing the transferability of adversarial examples with random patch.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Enhancing the transferability of adversarial examples with random patch

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.915245Z

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Observation 10db594a-3ca7-408e-9907-d0ae0775e47a · outbound

This paper cites Diffusion-based adversarial sample generation for improved stealthiness and controllability,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Diffusion-based adversarial sample generation for improved stealthiness and controllability,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.906252Z

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Observation 49033a04-1d33-405e-a1d3-354fa3ea71fb · outbound

This paper cites Adversarial machine learning at scale,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Adversarial machine learning at scale,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.896760Z

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Observation 1d1d2834-d3aa-4ee7-87ad-142759c33845 · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Feature squeezing: Detecting adversarial examples in deep neural networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.887350Z

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Observation 4b13b4c7-ba19-471c-9060-bdbf7165e8c9 · outbound

This paper cites Mitigating adversarial effects through randomization,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Mitigating adversarial effects through randomization,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.877953Z

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Observation cd355bc0-6f89-4960-ba1b-1b9284e9f9b6 · outbound

This paper cites Feature distillation: Dnn-oriented jpeg compression against adversarial examples,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Feature distillation: Dnn-oriented jpeg compression against adversarial examples,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.867794Z

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Observation f5d14aaa-c94f-4700-83ce-bedbd30540d2 · outbound

This paper cites A self- supervised approach for adversarial robustness,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation A self- supervised approach for adversarial robustness,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.857254Z

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Observation 945f4db5-b672-40ac-b6e4-1bef18f50b93 · outbound

This paper cites Score-based generative modeling through stochastic differ- ential equations,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Score-based generative modeling through stochastic differ- ential equations,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.847381Z

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Observation 72ba5001-4028-42bc-b5f8-a368132718d1 · outbound

This paper cites Randomized adversarial training via taylor expansion,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Randomized adversarial training via taylor expansion,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.837165Z

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Observation 47d4030a-15aa-4a12-b59c-ac58c19127b9 · outbound

This paper cites Taxonomy driven fast adversarial training,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Taxonomy driven fast adversarial training,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.826656Z

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Observation 3d09af20-b5eb-49f2-860e-c9dca367f4e2 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Learning multiple layers of features from tiny images,

Reference 70

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Observation 04a5de6d-aef2-42df-b85a-7b6974d50bff · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T11:52:01.376046Z

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Observation 2e1542c2-4eea-4f80-b100-7c682b983afa · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.811573Z

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Observation 5c038606-3231-4cb0-9ce6-9c26d4cefce3 · outbound

This paper cites Densely connected convolutional networks,.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Densely connected convolutional networks,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.802031Z

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Observation 2be4e5d1-d323-4055-b468-383e05983596 · outbound

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

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Inception-v4, inception-resnet and the impact of residual connections on learning,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:52:01.791857Z

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Observation 722368ab-419e-4712-b5ce-221789c28449 · outbound

This paper cites Available: https://doi.org/10.1109/TIFS.2019.2934069.

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation Available: https://doi.org/10.1109/TIFS.2019.2934069

Reference 2020

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