Paper Citation Record · LEDGER

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art

As of 19 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2505.08552.

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

pith.paper-citation-record.v1
2505.08552 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:55:17.467738Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:55:17.369017Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T21:55:17.598170Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy11
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 488bd85b-8509-41d9-9348-da6101771a3b · outbound

This paper cites an unresolved cited work.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:55:17.827529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 606a5613-67c4-4140-a548-e3597a28d0b9 · outbound

This paper cites an unresolved cited work.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:55:17.813525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1570dc26-a66c-47f2-b8cc-e1e6913e032f · outbound

This paper cites an unresolved cited work.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:55:17.799837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9f28c797-d2a3-4699-ac78-4885736a5b22 · outbound

This paper cites DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T21:55:17.602979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 10233b7b-c6f6-47ed-8544-781f4943e665 · outbound

This paper cites We first present DFA-CON, a contrastive repre- sentation learning framework designed to detect copyright in- fringement in AI-generated art (see Fig.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art We first present DFA-CON, a contrastive repre- sentation learning framework designed to detect copyright in- fringement in AI-generated art (see Fig

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.785269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e683ea46-e8fd-4241-8ac6-30d72ac53a1b · outbound

This paper cites A similarity threshold is first determined using validation set and then applied during test- ing to make binary decisions.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art A similarity threshold is first determined using validation set and then applied during test- ing to make binary decisions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.769904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52c85a67-34d4-4568-9881-049310f3f221 · outbound

This paper cites Results indicate that using embeddings directly from the encoder output in R2048 yields the highest scores across all metrics.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Results indicate that using embeddings directly from the encoder output in R2048 yields the highest scores across all metrics

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.754748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19ff3d1c-a3da-4d33-8b0d-be1c42d800a5 · outbound

This paper cites Our method leverages forgery-aware sam- pling and contrastive representation learning to distinguish original artworks from their forged counterparts.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Our method leverages forgery-aware sam- pling and contrastive representation learning to distinguish original artworks from their forged counterparts

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.739501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e852aa1-35c6-4dd3-a547-1a33c95491a2 · outbound

This paper cites Ai art and its impact on artists,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Ai art and its impact on artists,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.724573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a34a844e-40d6-4509-8377-933cb1599740 · outbound

This paper cites On memoriza- tion in probabilistic deep generative models,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art On memoriza- tion in probabilistic deep generative models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.710511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed293748-2cb9-4fef-a5c8-6de1aaf2963b · outbound

This paper cites Diffusion art or digital forgery? investigating data replication in diffu- sion models,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Diffusion art or digital forgery? investigating data replication in diffu- sion models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.695863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 488dee74-0039-4fc2-9a77-f2ac20db93c9 · outbound

This paper cites DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:55:17.582234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aac4b13e-c2de-4dca-9c53-9af619c7dfc3 · outbound

This paper cites CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.422108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d08f09-c13d-4d35-9ab6-7cd2a5e770a6 · outbound

This paper cites High-resolution im- age synthesis with latent diffusion models,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art High-resolution im- age synthesis with latent diffusion models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.426916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40bd4c2c-9460-4eb0-bc74-bfa0496b955e · outbound

This paper cites DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.431339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df33fd5-28e1-4d4f-8b11-31f8708b5485 · outbound

This paper cites Contrastive representation learning: A framework and review,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Contrastive representation learning: A framework and review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.672405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cebb7d38-f696-4264-8a50-c74c16ae6dc4 · outbound

This paper cites Supervised contrastive learning,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Supervised contrastive learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.657194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25f25845-d8ca-4d0a-bf58-756ef86c0014 · outbound

This paper cites SolidMark: Evaluating Image Memorization in Generative Models.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art SolidMark: Evaluating Image Memorization in Generative Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.445000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d65a316-e7e9-4f30-bdd4-0a61aa4e37a3 · outbound

This paper cites Artistic style transfer with internal-external learning and contrastive learning,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Artistic style transfer with internal-external learning and contrastive learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.642967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf355074-4b1c-4838-9970-8c7a096aabdd · outbound

This paper cites Deep residual learning for image recognition,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Deep residual learning for image recognition,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.454082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90ad294e-02c2-4f30-86f1-f8a4d4bc8e66 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.458738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d85e479a-cb98-4d14-9803-63575d901774 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art DINOv2: Learning Robust Visual Features without Supervision

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:17.463087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e22553b6-59ae-42f1-af11-acb52cab4585 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision,.

DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art Learning transferable visual models from natural lan- guage supervision,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:55:17.617721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 9f28c797-d2a3-4699-ac78-4885736a5b22 · inbound