Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense | Paper Citation Record

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This paper cites Towards improving robustness of deep neural networks to adversarial perturbations.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Towards improving robustness of deep neural networks to adversarial perturbations

Reference 1

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This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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This paper cites Defense against adversarial attacks using dragan.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Defense against adversarial attacks using dragan

Reference 3

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This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 4

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This paper cites Synthesizing robust adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Synthesizing robust adversarial examples

Reference 5

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This paper cites Parameter-free online test-time adaptation.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Parameter-free online test-time adaptation

Reference 6

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Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Towards evaluating the robustness of neural networks

Reference 7

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This paper cites Robust classification via a single diffusion model.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Robust classification via a single diffusion model

Reference 8

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Observation 9e7350ed-b093-46fe-9e41-eb85486a7936 · outbound

This paper cites Robust overfitting may be mitigated by properly learned smoothening.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Robust overfitting may be mitigated by properly learned smoothening

Reference 9

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

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense A simple framework for contrastive learning of visual representations

Reference 10

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Observation fb3acd87-cd4d-480f-a2a2-dd21ffee5a2c · outbound

This paper cites Evaluating the adversarial robustness of adaptive test-time defenses.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Evaluating the adversarial robustness of adaptive test-time defenses

Reference 11

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This paper cites Minimally distorted adversarial ex- amples with a fast adaptive boundary attack.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Minimally distorted adversarial ex- amples with a fast adaptive boundary attack

Reference 12

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Observation dd26f23b-e6b8-48ac-a6f2-f3f840e38f83 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 13

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This paper cites Libre: A practical bayesian approach to adversarial detection.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Libre: A practical bayesian approach to adversarial detection

Reference 14

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This paper cites The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training

Reference 15

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This paper cites Boosting adversarial attacks with momentum.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Boosting adversarial attacks with momentum

Reference 16

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This paper cites Enhancing the robustness of neural collaborative filtering systems under malicious attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Enhancing the robustness of neural collaborative filtering systems under malicious attacks

Reference 17

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Observation c2d574a2-d3be-42da-965e-3db4fa0e3e0e · outbound

This paper cites Unsupervised image captioning.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Unsupervised image captioning

Reference 18

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Observation f6819be6-aaba-44f8-9c6d-a7bb569f5955 · outbound

This paper cites Push & pull: Transferable adversarial examples with attentive attack.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Push & pull: Transferable adversarial examples with attentive attack

Reference 19

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This paper cites Unsupervised representation learning by predicting image rotations.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Unsupervised representation learning by predicting image rotations

Reference 20

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This paper cites Explaining and harnessing adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Explaining and harnessing adversarial examples

Reference 21

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This paper cites Momentum contrast for unsupervised visual representation learning.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Momentum contrast for unsupervised visual representation learning

Reference 22

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This paper cites Deep residual learning for image recognition.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Deep residual learning for image recognition

Reference 23

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Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Identity mappings in deep residual networks

Reference 24

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This paper cites Aid-purifier: A light auxiliary network for boosting adversarial defense.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Aid-purifier: A light auxiliary network for boosting adversarial defense

Reference 25

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This paper cites Puvae: A variational autoencoder to purify adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Puvae: A variational autoencoder to purify adversarial examples

Reference 26

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

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Learning multiple layers of features from tiny images

Reference 27

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This paper cites Adversarial machine learning at scale.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Adversarial machine learning at scale

Reference 28

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This paper cites Gradient-based learning applied to document recognition.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Gradient-based learning applied to document recognition

Reference 29

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This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 30

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This paper cites Robust evaluation of diffusion-based adversarial purification.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Robust evaluation of diffusion-based adversarial purification

Reference 31

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This paper cites Learn- ing defense transformations for counterattacking adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Learn- ing defense transformations for counterattacking adversarial examples

Reference 32

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This paper cites Dual manifold adversarial robustness: Defense against lp and non-lp adversarial attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Dual manifold adversarial robustness: Defense against lp and non-lp adversarial attacks

Reference 33

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This paper cites Characterizing adversarial subspaces using local intrinsic dimensionality.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Characterizing adversarial subspaces using local intrinsic dimensionality

Reference 34

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

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Towards deep learning models resistant to adversarial attacks

Reference 35

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This paper cites Adversarial attacks are reversible with natural supervision.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Adversarial attacks are reversible with natural supervision

Reference 36

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This paper cites Guessing and entropy.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Guessing and entropy

Reference 37

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This paper cites Toward robust sensing for autonomous vehicles: An adversarial perspective.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Toward robust sensing for autonomous vehicles: An adversarial perspective

Reference 38

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Deepfool: a simple and accurate method to fool deep neural networks

Reference 39

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This paper cites Diffusion models for adversarial purification.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Diffusion models for adversarial purification

Reference 40

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This paper cites Overfitting in adversarially robust deep learning.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Overfitting in adversarially robust deep learning

Reference 41

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This paper cites Defense-gan: Protecting classifiers against adversarial attacks using generative models.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Defense-gan: Protecting classifiers against adversarial attacks using generative models

Reference 42

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This paper cites Online adversarial purification based on self-supervised learning.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Online adversarial purification based on self-supervised learning

Reference 43

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This paper cites Pixeldefend: Leveraging generative models to understand and defend against adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Pixeldefend: Leveraging generative models to understand and defend against adversarial examples

Reference 44

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This paper cites Test-time training for out-of-distribution generalization.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Test-time training for out-of-distribution generalization

Reference 45

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This paper cites Intriguing properties of neural networks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Intriguing properties of neural networks

Reference 46

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This paper cites Robust overfitting does matter: Test-time adversarial purification with fgsm.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Robust overfitting does matter: Test-time adversarial purification with fgsm

Reference 47

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This paper cites Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder

Reference 48

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This paper cites Average gradient-based adversarial attack.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Average gradient-based adversarial attack

Reference 49

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Observation 8a9688d8-4ca3-479c-95d0-c2b774d731fc · outbound

This paper cites Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks

Reference 50

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This paper cites Tent: Fully test-time adaptation by entropy minimization.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Tent: Fully test-time adaptation by entropy minimization

Reference 51

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This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Improving adversarial robustness requires revisiting misclassified examples

Reference 52

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Observation e34e9ffa-d43f-4d6a-a7bf-3b8967b6dbf0 · outbound

This paper cites Better diffusion models further improve adversarial training.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Better diffusion models further improve adversarial training

Reference 53

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Observation 9c51a591-f94b-448b-ad92-6b0d638c94f7 · outbound

This paper cites Towards robust person re-identification by adversarial training with dynamic attack strategy.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Towards robust person re-identification by adversarial training with dynamic attack strategy

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.170559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d58a8896-0a57-41af-86f9-0c560663eb8d · outbound

This paper cites Improving vaes’ robustness to adversarial attack.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Improving vaes’ robustness to adversarial attack

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.151849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8fc2c2d-14a9-4284-87d3-65c635d8f364 · outbound

This paper cites Fast is better than free: Revisiting adversarial training.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Fast is better than free: Revisiting adversarial training

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.132243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a8ec3aa8-f724-46fc-9ae3-1febff07c695 · outbound

This paper cites DensePure: Understanding Diffusion Models towards Adversarial Robustness.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense DensePure: Understanding Diffusion Models towards Adversarial Robustness

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T20:15:51.649141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb27837b-df60-47ff-8045-30d46e96ce49 · outbound

This paper cites Spatially transformed adversarial examples.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Spatially transformed adversarial examples

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.112846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 76793e65-1732-4c76-a323-885e72168fba · outbound

This paper cites Adversarial attack against urban scene segmentation for autonomous vehicles.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Adversarial attack against urban scene segmentation for autonomous vehicles

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.094691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2cd1cef2-377f-4fe3-95da-9b7ee9478728 · outbound

This paper cites Exact adversarial attack to image captioning via structured output learning with latent variables.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Exact adversarial attack to image captioning via structured output learning with latent variables

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.074692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c260c79-12d3-4f94-bef6-59001898c528 · outbound

This paper cites Class-disentanglement and applications in adversarial detection and defense.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Class-disentanglement and applications in adversarial detection and defense

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.051157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4eb8c5f8-b83c-43ba-8115-7cc1a50b0a9d · outbound

This paper cites Adversarial purification with the manifold hypothesis.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Adversarial purification with the manifold hypothesis

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.031458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e66b31c4-7245-4ae7-98ab-aa4bbd609380 · outbound

This paper cites Defending against adversarial attacks using spherical sampling-based variational auto- encoder.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Defending against adversarial attacks using spherical sampling-based variational auto- encoder

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:52.010151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23180848-633f-4ace-ad15-266a0e10aea0 · outbound

This paper cites Adversarial purification with score-based generative models.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Adversarial purification with score-based generative models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.988809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5aae5aa0-828b-4605-81ff-3caf547133fa · outbound

This paper cites Automa: Towards automatic model augmentation for transferable adversarial attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Automa: Towards automatic model augmentation for transferable adversarial attacks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.968846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f02afb0-398a-4730-aa88-748e6a27a04b · outbound

This paper cites Wide Residual Networks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Wide Residual Networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T20:15:51.691529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7525d0b-f5ef-4781-9f17-b06bcba2a263 · outbound

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

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Theoretically principled trade-off between robustness and accuracy

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T20:15:51.697269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdf0e505-4846-44ee-9eed-1e30e6cc67d8 · outbound

This paper cites Meta invariance defense towards generalizable robustness to unknown adversarial attacks.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Meta invariance defense towards generalizable robustness to unknown adversarial attacks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.938026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14137e25-2246-4cab-aa0c-f49036fa8ae6 · outbound

This paper cites Memo: Test time robustness via adaptation and augmentation.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Memo: Test time robustness via adaptation and augmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.919478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bee4fcc-6c85-4798-a7f6-da7794285d47 · outbound

This paper cites Detecting adversarial data by probing mul- tiple perturbations using expected perturbation score.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Detecting adversarial data by probing mul- tiple perturbations using expected perturbation score

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.896469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 02a56ebb-8f28-42c4-b0ab-3b8a11a96c9a · outbound

This paper cites Robust physical-world attacks on face recognition.

Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense Robust physical-world attacks on face recognition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:15:51.878229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

Outbound references

Pith citing papers