Paper Citation Record · LEDGER

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2602.05175.

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

pith.paper-citation-record.v1
2602.05175 v2

Coverage vector

measured 22 of 22 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-03T04:23:55.308307Z

measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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Outbound references

Observation abef4d93-a9dc-4fbf-bf36-1217697c3bff · outbound

This paper cites Meansparse: Post-training robust- ness enhancement through mean-centered feature sparsifica- tion.CoRR, 2024.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Meansparse: Post-training robust- ness enhancement through mean-centered feature sparsifica- tion.CoRR, 2024

Reference 1

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Observation 370bc6e9-9d21-4ddf-8523-9afe8205160e · outbound

This paper cites Diffusion models demand contrastive guidance for adversarial purification to advance.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Diffusion models demand contrastive guidance for adversarial purification to advance

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:53.164454Z

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Observation 05f580e8-3cb7-4316-a3cf-4b7fc8871105 · outbound

This paper cites Robust one-class classification with signed distance function using 1-Lipschitz neural networks.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Robust one-class classification with signed distance function using 1-Lipschitz neural networks

Reference 3

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Observation 031b2921-619b-49cc-b8bd-27cf686ebf82 · outbound

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

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 4

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Observation e7092935-f1f5-453b-a35d-0320c0bc6834 · outbound

This paper cites Wichmann, and Wieland Bren- del.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Wichmann, and Wieland Bren- del

Reference 5

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Observation 2d3b98a5-ff17-4bd4-934e-57d37cd951a1 · outbound

This paper cites Im- proving robustness using generated data.Advances in neural information processing systems, 34:4218–4233, 2021.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Im- proving robustness using generated data.Advances in neural information processing systems, 34:4218–4233, 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:53.739989Z

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Observation e56e417e-20b4-429b-8227-64caf241e33e · outbound

This paper cites Deep residual learning for image recognition.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Deep residual learning for image recognition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:53.896019Z

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Observation 65ff96f3-a65e-48ad-8011-48fb84d432f9 · outbound

This paper cites Adversar- ial examples are not bugs, they are features.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Adversar- ial examples are not bugs, they are features

Reference 8

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Observation 162c257b-7f45-4bcb-ab8e-86dfb178ec3f · outbound

This paper cites Jackson, Amir Atapour-Abarghouei, Stephen Bon- ner, Toby P.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Jackson, Amir Atapour-Abarghouei, Stephen Bon- ner, Toby P

Reference 9

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Observation abb9362e-fccb-4470-95bb-a0f6de4d741f · outbound

This paper cites Interpolated joint space adversarial training for robust and generalizable defenses.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Interpolated joint space adversarial training for robust and generalizable defenses

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.126686Z

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Observation 27e9a4f0-54f3-45fa-8f8a-44f820f3d858 · outbound

This paper cites Instant adversarial purification with adversarial consistency distillation.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Instant adversarial purification with adversarial consistency distillation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.191033Z

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Observation 6a3469d5-f0d3-4c1a-956a-6ad6f431a224 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Swin transformer: Hierarchical vision transformer using shifted windows

Reference 12

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Observation d71533b1-aa81-4cf4-af40-ec998ac94611 · outbound

This paper cites A convnet for the 2020s.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture A convnet for the 2020s

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.337567Z

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Observation d569378a-a5d0-4eb6-ae38-8e67b64b5e7e · outbound

This paper cites Towards deep learn- ing models resistant to adversarial attacks.The International Conference on Learning Representations (ICLR), 2018.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Towards deep learn- ing models resistant to adversarial attacks.The International Conference on Learning Representations (ICLR), 2018

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.391874Z

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Observation 2c001f9a-07cc-4d13-a13e-4155448f6715 · outbound

This paper cites Diffusion Models for Adversarial Purification.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Diffusion Models for Adversarial Purification

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.479153Z

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Observation 514cda13-a184-4cd3-97a4-c70d0edc7484 · outbound

This paper cites Fronts propagating with curvature-dependent speed: Algorithms based on hamilton- jacobi formulations.Journal of Computational Physics, 79 (1):12–49, 1988.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Fronts propagating with curvature-dependent speed: Algorithms based on hamilton- jacobi formulations.Journal of Computational Physics, 79 (1):12–49, 1988

Reference 16

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Observation 242b645c-f7d1-408a-b565-ea32ba29e4cf · outbound

This paper cites Causality-inspired single- source domain generalization for medical image segmenta- tion.IEEE Transactions on Medical Imaging, 42(4):1095– 1106, 2022.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Causality-inspired single- source domain generalization for medical image segmenta- tion.IEEE Transactions on Medical Imaging, 42(4):1095– 1106, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.657896Z

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Observation 5c69ab77-9788-4036-94b8-b016916881ef · outbound

This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Deepsdf: Learning con- tinuous signed distance functions for shape representation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.755780Z

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Observation 1ce59bd7-b8e8-4d36-90dd-d0a290e6fea3 · outbound

This paper cites Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:54.875253Z

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Observation bf174008-2206-4202-b06b-f4237377ef34 · outbound

This paper cites Towards the first adversarially robust neural net- work model on MNIST.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Towards the first adversarially robust neural net- work model on MNIST

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:55.080894Z

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Observation f61cfcf4-331b-4289-b002-3175c63a62e7 · outbound

This paper cites Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models.Advances in Neural Information Processing Systems, 36:13931–13955,.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Revisiting adversarial training for imagenet: Architectures, training and generalization across threat models.Advances in Neural Information Processing Systems, 36:13931–13955,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:55.182538Z

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Observation 2e51cacf-8a4a-43d0-a77a-24b9071f3306 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture Guided Diffusion Model for Adversarial Purification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T04:23:55.308307Z

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