Paper Citation Record · LEDGER

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2502.07225.

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

pith.paper-citation-record.v1
2502.07225 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:30:44.209229Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 671d717a-7a21-4561-956a-99868b9c2dc1 · outbound

This paper cites IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.823066Z

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 bcb789f3-07db-4d64-9a77-ca191e5b1f29 · outbound

This paper cites M., and Zisserman, A.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models M., and Zisserman, A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.808869Z

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 1eb87164-7478-419f-a48e-46c33ab310a4 · outbound

This paper cites Extracting Training Data from Diffusion Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Extracting Training Data from Diffusion Models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.794910Z

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 0ab499b1-849e-4b01-aa8e-f2bd897227a0 · outbound

This paper cites and Mo, J.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models and Mo, J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.781136Z

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 5400f1fb-7a97-4340-b60b-29546d234b4b · outbound

This paper cites TopIQ: A Top-Down Approach from Semantics to Distortions for Image Quality Assessment.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models TopIQ: A Top-Down Approach from Semantics to Distortions for Image Quality Assessment

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.767815Z

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 570326c0-9a09-4139-b4d1-8e3be14ba8ce · outbound

This paper cites ArcFace: Additive Angular Margin Loss for Deep Face Recognition.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.754021Z

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 25c74110-9343-47af-b4e9-8e2930db416f · outbound

This paper cites RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.740271Z

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 aa490617-9f01-4351-8935-70edc8801221 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.726997Z

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 f86b5308-a8ae-40de-8db0-1728da6fdbd1 · outbound

This paper cites and Nichol, A.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models and Nichol, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.713554Z

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 f14ca758-1364-4763-a1bf-f1a7bab60d78 · outbound

This paper cites Meet The Three Artists Behind A Landmark Lawsuit Against AI Art Generators , 2023.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Meet The Three Artists Behind A Landmark Lawsuit Against AI Art Generators , 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.700289Z

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 8e568dba-4d76-40a8-b30c-596418d3c2b3 · outbound

This paper cites J., Shlens, J., and Szegedy, C.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models J., Shlens, J., and Szegedy, C

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.686736Z

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 57f96a6c-1dfb-4c0e-a1ae-612bbdb2a426 · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.673276Z

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 349252cb-fa12-4e38-81d0-291b5dcfc3d6 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Denoising Diffusion Probabilistic Models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.655756Z

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 1b7088c3-0a2f-483f-b506-a2d221c56910 · outbound

This paper cites Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.641782Z

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 b418a6ae-b8ee-4b4b-850b-90542aad5748 · outbound

This paper cites J., Shen, Y., Wallis, P., Allen - Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models J., Shen, Y., Wallis, P., Allen - Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.627941Z

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 34e5f897-fbe7-4bcb-94ed-2dcff0bae00f · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.614440Z

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 10806c88-c04a-4136-9b8e-3cc0b0a75aae · outbound

This paper cites Imagic: Text-Based Real Image Editing with Diffusion Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Imagic: Text-Based Real Image Editing with Diffusion Models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.600532Z

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 6808ff05-3380-447f-92ef-0305a9695f37 · outbound

This paper cites V., Phung, H., Nguyen, T.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models V., Phung, H., Nguyen, T

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.587065Z

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 3536a557-ed73-41f1-8c0f-aad1f4fb1382 · outbound

This paper cites Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.573746Z

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 454537b1-3825-4125-82b7-8b94e0091e62 · outbound

This paper cites Mist: Towards Improved Adversarial Examples for Diffusion Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Mist: Towards Improved Adversarial Examples for Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:44.113526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec43f6c-cb13-4e1a-9a04-ce1477c4da12 · outbound

This paper cites Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.559907Z

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 5c0dd4f1-279a-4f7f-ac2a-66a6d8971630 · outbound

This paper cites MetaCloak: Preventing Unauthorized Subject-Driven Text-to-Image Diffusion-Based Synthesis via Meta-Learning.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models MetaCloak: Preventing Unauthorized Subject-Driven Text-to-Image Diffusion-Based Synthesis via Meta-Learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.545872Z

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 772797be-0f33-4962-bd24-acfb6f17f953 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.531984Z

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 b475f80b-12ea-4ab6-84e3-d6d14a315836 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.518768Z

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 aa5cc02e-7035-41a1-8581-01accff1a781 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.504762Z

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 2621801b-e521-425a-bc12-dd698525ac5b · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:44.140570Z

Source-reported events for the cited work

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Observation a2d0682a-2e7a-48f5-83e2-e772948fdfc6 · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.490737Z

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 ff5a4ec6-96f4-45fb-ab1e-2b6695bf95a6 · outbound

This paper cites Diffusion Models for Adversarial Purification.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Diffusion Models for Adversarial Purification

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.476725Z

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 2588e70a-d48b-44ec-88a9-22567ebd2f65 · outbound

This paper cites Protective Perturbations Against Unauthorized Data Usage in Diffusion-Based Image Generation.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Protective Perturbations Against Unauthorized Data Usage in Diffusion-Based Image Generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.462975Z

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 5580d35b-f959-45d8-8a58-584602813e7b · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.448482Z

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 745334b0-cd76-44bb-a497-a04b0a9efc69 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.434733Z

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 f977dc2b-043b-4a4b-994b-036bb1f6a3cb · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.420330Z

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 884e384f-36f7-4039-8558-80d902d7b7a4 · outbound

This paper cites A., Ho, J., Salimans, T., Fleet, D.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models A., Ho, J., Salimans, T., Fleet, D

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.406362Z

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 46d7b026-42e2-4a4e-a0c5-b2fab5eb5839 · outbound

This paper cites Raising the Cost of Malicious AI -Powered Image Editing.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Raising the Cost of Malicious AI -Powered Image Editing

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.392118Z

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 38141475-4d10-48c1-8c2d-76e4fdbd7d70 · outbound

This paper cites D., Croce, F., and Hein, M.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models D., Croce, F., and Hein, M

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.377015Z

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 ddbc6d77-31ab-4595-9a4b-960c85faa7c4 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.362050Z

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 719ad098-bb9b-43cd-92e7-ad1b263b587e · outbound

This paper cites Stability AI Image Models — Stability AI , 2024.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Stability AI Image Models — Stability AI , 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.346256Z

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 d2b34c05-d90a-4215-a3de-d4bca34ef6ba · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.331211Z

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 09d90159-aaa8-4c08-9f28-da66208d6b9e · outbound

This paper cites Toward Effective Protection Against Diffusion-Based Mimicry Through Score Distillation.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Toward Effective Protection Against Diffusion-Based Mimicry Through Score Distillation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.316454Z

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 e2baa408-fb5b-4b07-afc0-37cedc01aca7 · outbound

This paper cites Inversion-Based Style Transfer with Diffusion Models.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Inversion-Based Style Transfer with Diffusion Models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:44.301973Z

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 7f2646ee-9324-4ccc-aab4-abfbd751acc5 · outbound

This paper cites an unresolved cited work.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:30:44.286272Z

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 66417569-b7bb-4359-addf-ec92733b0f33 · outbound

This paper cites write newline.

CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models write newline

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:44.209229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.