Paper Citation Record · LEDGER

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study

As of 15 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2507.03953.

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

pith.paper-citation-record.v1
2507.03953 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:03:52.156580Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy3
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22e61c80-0d72-460f-8e52-e795e59aeeec · outbound

This paper cites M., and Zisserman, A.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study M., and Zisserman, A

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:53.313137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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

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

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 4aaa690f-3209-4f24-b444-3636adff6ac1 · outbound

This paper cites DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:03:52.423968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ef32539-2fa3-445d-8bc3-7dc7c4c4790d · outbound

This paper cites Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:51.700880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 173cde61-1535-4d7b-991c-405b565f1608 · outbound

This paper cites Defending against gan-based deepfake attacks via transformation-aware ad- versarial faces.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Defending against gan-based deepfake attacks via transformation-aware ad- versarial faces

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:52.913405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7a090a83-721e-4f8e-ba76-358cd55ab7b5 · outbound

This paper cites an unresolved cited work.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:52.668574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 24a2413d-3b5b-45de-8c10-d64f684a7db6 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Multi-concept customization of text-to-image diffusion

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:53.055999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d7ce3a0-0ddd-49dd-99e0-d438052332e9 · outbound

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

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Raising the Cost of Malicious AI-Powered Image Editing

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:51.831233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88228616-1782-44ae-adc8-87e2c92cd9c3 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Classifier-Free Diffusion Guidance

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:50.754550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d922409-d6b7-4605-82cf-edbd19421035 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:50.684234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8acb81ab-0874-4124-b597-6fc9c42ff694 · outbound

This paper cites Auto-Encoding Variational Bayes.

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Auto-Encoding Variational Bayes

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:50.927741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d034727-abcc-45d1-ab47-9d98d92102c3 · outbound

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

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study Mist: Towards Improved Adversarial Examples for Diffusion Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:51.209198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.