Paper Citation Record · LEDGER

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2302.04578.

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

pith.paper-citation-record.v1
2302.04578 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:24:00.493067Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:08:36.787210Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 10901055-7300-476e-8922-20accd288260 · inbound

Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection cites this paper.

Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:00.493067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aae345cd-2d4d-483b-8d1b-acf6ac22240d · inbound

Is Perturbation-Based Image Protection Disruptive to Image Editing? cites this paper.

Is Perturbation-Based Image Protection Disruptive to Image Editing? Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:46:07.877439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1074cfd-62eb-4d4d-be7c-74c222dc2aa9 · inbound

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study cites this paper.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:51.343454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad151494-efad-4603-b5da-2ee1c3855a79 · inbound

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media cites this paper.

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T17:29:06.765795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e66b22be-38b3-436e-9b56-61ec20005a51 · inbound

Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization cites this paper.

Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:37:25.021029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0ebaf98f-4e61-4eb1-9698-4d71c3b93f0c · inbound

VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models cites this paper.

VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:07:56.146775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da0b75cc-b05b-4e52-a2e0-0a06c255e8ac · inbound

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models cites this paper.

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:08:36.788795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 287d81f0-304c-4c87-8a80-721ac577dc9e · inbound

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models cites this paper.

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-12T08:29:25.498449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a928b0e-3d4f-48c3-be5b-781f73826b77 · inbound

Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization cites this paper.

Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T05:32:29.584934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.