Paper Citation Record · LEDGER

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2502.03758.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.03758 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:55:56.378999Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5cee90da-1cf1-4ebc-9d4e-8640be608981 · outbound

This paper cites Goodfellow, and Rob Fergus.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Goodfellow, and Rob Fergus

Reference 1

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Observation ec6249aa-49af-41d9-ae7e-7ea89aa1a8c2 · outbound

This paper cites Spatially transformed adversarial examples.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Spatially transformed adversarial examples

Reference 2

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Observation a645b60f-7f32-431d-ba1c-b8c37d68681a · outbound

This paper cites Semantic-preserving adversarial text attacks.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Semantic-preserving adversarial text attacks

Reference 3

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Observation efcd574a-92bf-4128-86d7-c3b1f483d749 · outbound

This paper cites Two-face: Adversarial audit of commercial face recognition systems.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Two-face: Adversarial audit of commercial face recognition systems

Reference 4

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Observation 3da5fa51-f2d2-40b4-8ab1-af53f9573f0a · outbound

This paper cites Adversarial examples based on object detection tasks: A survey.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial examples based on object detection tasks: A survey

Reference 5

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Observation afc46ce7-fd95-4eb0-a345-d4d6d1d453ef · outbound

This paper cites Exploring the feasibility of adversarial attacks on medical image segmentation.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Exploring the feasibility of adversarial attacks on medical image segmentation

Reference 6

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Observation 7e151241-7262-4b62-af0f-141ffe594c58 · outbound

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

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Towards deep learning models resistant to adversarial attacks

Reference 7

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Observation 67f4f9cc-3a32-40cb-9b89-0f3ce0576669 · outbound

This paper cites Adversarial training with complementary labels: on the benefit of gradually informative attacks.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial training with complementary labels: on the benefit of gradually informative attacks

Reference 8

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Observation 0ae06fbe-f856-41ea-b162-a9d9935aa33b · outbound

This paper cites Adversarial robustness in graph neural networks: A hamiltonian approach.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial robustness in graph neural networks: A hamiltonian approach

Reference 9

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Observation aa360ac3-af19-4237-84b5-eaa6c3b8db25 · outbound

This paper cites Inspector for face forgery detection: Defending against adversarial attacks from coarse to fine.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Inspector for face forgery detection: Defending against adversarial attacks from coarse to fine

Reference 10

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Observation b3f0b346-e9e9-486b-926b-772517d4bc90 · outbound

This paper cites Improving adversarial robustness of masked autoencoders via test-time frequency-domain prompting.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Improving adversarial robustness of masked autoencoders via test-time frequency-domain prompting

Reference 11

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Observation ecad9518-9602-4b41-bd4a-d75f7d8bed15 · outbound

This paper cites Visual prompting for adversarial robustness.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Visual prompting for adversarial robustness

Reference 12

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Observation 592db693-6868-49b7-a683-1df66cf9eee1 · outbound

This paper cites Adversarial weight perturbation helps robust generalization.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial weight perturbation helps robust generalization

Reference 13

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Observation c8c1ff38-4ae3-4e0d-a886-4ba466dd9876 · outbound

This paper cites Cfa: Class-wise calibrated fair adversarial training.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Cfa: Class-wise calibrated fair adversarial training

Reference 14

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Observation 60c3a401-8d20-49fd-9e34-93a3c424a053 · outbound

This paper cites Revisiting adversarial training for imagenet: Architec- tures, training and generalization across threat models.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Revisiting adversarial training for imagenet: Architec- tures, training and generalization across threat models

Reference 15

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Observation c4d3c428-8df6-4787-a645-f2c5f417750e · outbound

This paper cites Diffusion Models for Adversarial Purification.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Diffusion Models for Adversarial Purification

Reference 16

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Observation f35bdc5b-9227-43df-b62d-ec4855b7e1e7 · outbound

This paper cites Eliminating adversarial noise via information discard and robust representation restoration.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Eliminating adversarial noise via information discard and robust representation restoration

Reference 17

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Observation 2955db6a-bd28-45f6-bb0e-00d8ce952cc1 · outbound

This paper cites Filtering for texture classification: A comparative study.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Filtering for texture classification: A comparative study

Reference 18

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This paper cites Texture classification using wavelet transform and support vector machines.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Texture classification using wavelet transform and support vector machines

Reference 19

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Observation 0178e9ca-2ef6-4671-98f7-469f9ff7519a · outbound

This paper cites Phase congruency: A low-level image invariant.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Phase congruency: A low-level image invariant

Reference 20

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Observation 813d4d36-7b02-48be-bc4b-d5712b05fd60 · outbound

This paper cites Fsim: A feature similarity index for image quality assessment.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Fsim: A feature similarity index for image quality assessment

Reference 21

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Observation a8672c30-5f34-45b3-b188-ec8a88bcb991 · outbound

This paper cites Metamers of the ventral stream.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Metamers of the ventral stream

Reference 22

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Observation 01b54e19-650f-4dc7-9a9e-52544dfa73ad · outbound

This paper cites On the role of spatial phase and phase correlation in vision, illusion, and cognition.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting On the role of spatial phase and phase correlation in vision, illusion, and cognition

Reference 23

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This paper cites Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain

Reference 24

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

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 25

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Towards evaluating the robustness of neural networks

Reference 26

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This paper cites Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses

Reference 27

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

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Square attack: a query- efficient black-box adversarial attack via random search

Reference 28

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Observation 71725fa4-c837-4efa-94eb-c132453b691d · outbound

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

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Theoretically principled trade-off between robustness and accuracy

Reference 29

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Improving adversarial robustness requires revisiting misclassified examples

Reference 30

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Ape-gan: Adversarial perturbation elimination with gan

Reference 31

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Texture and shape synthesis on surfaces

Reference 32

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Learning lbp structure by maximizing the conditional mutual information

Reference 33

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Learning multiple layers of features from tiny images

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Tiny imagenet visual recognition challenge

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Deep residual learning for image recognition

Reference 36

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Wide Residual Networks

Reference 37

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Very Deep Convolutional Networks for Large-Scale Image Recognition

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Swin transformer: Hierarchical vision transformer using shifted windows

Reference 39

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Scalable training of l 1-regularized log-linear models

Reference 40

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial self-supervised contrastive learning

Reference 41

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Robust pre-training by adversarial contrastive learning

Reference 42

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Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Enhancing adversarial contrastive learning via adversarial invariant regularization

Reference 43

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This paper cites Exploring the relationship between architectural design and adversarially robust generalization.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Exploring the relationship between architectural design and adversarially robust generalization

Reference 44

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Observation d44e30c1-cdb0-4653-afcc-d86e245f5809 · outbound

This paper cites Adversarial Reprogramming of Neural Networks.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Adversarial Reprogramming of Neural Networks

Reference 45

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This paper cites Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:55:56.483494Z

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This paper cites Fairness repro- gramming.

Improving Adversarial Robustness via Phase and Amplitude-aware Prompting Fairness repro- gramming

Reference 47

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Pith citing papers

No inbound Pith citation observations are available.