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Paper Citation Record · LEDGER

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 4 inbound Pith citation observations for arXiv:2506.00874.

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

pith.paper-citation-record.v1
2506.00874 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:01:39.698639Z

measured 45 of 45 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:25:15.742028Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T20:55:15.183855Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved30
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd99ab4f-ca9a-418d-abe0-d0483ef50a5f · outbound

This paper cites Fakeinversion: Learning to detect images from unseen text-to-image models by inverting stable diffusion.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Fakeinversion: Learning to detect images from unseen text-to-image models by inverting stable diffusion

Reference 1

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

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Observation 5745a091-62e9-43c9-8ae9-5a582c8c67b7 · outbound

This paper cites DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection DRCT: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images

Reference 2

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raw_fallback, observed 2026-08-07T12:01:40.806509Z

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

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Observation dccb8a31-c87d-4e86-93c0-ddca62876af2 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Xception: Deep learning with depthwise separable convolutions

Reference 3

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source=pdf_text observed=2026-08-07T12:01:36.126998Z digest=sha256:fa723499c23f6eb24f6fe1ddf8cd35ed2a05dd91c509c8a583cf7d0ccd87de0b

Observation a79f69d1-72fb-4443-9940-fd92c7efe11c · outbound

This paper cites Scaling instruction-finetuned language models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Scaling instruction-finetuned language models

Reference 4

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source=pdf_text observed=2026-08-07T12:01:36.208536Z digest=sha256:cb54429da08600367b2d8f519cf0f91024e442880effe711e43d73e08b3cc396

Observation e0931b02-18dc-4876-bfc8-97323ac0b67e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Imagenet: A large-scale hierarchical image database

Reference 5

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source=pdf_text observed=2026-08-07T12:01:36.281828Z digest=sha256:3e0ed71554fc54be030bbdc5af0e9dd97319046606a563939e4e2e2c3d104e26

Observation 36d7f5b2-eb54-4a98-880f-e0e7c1ab5e3d · outbound

This paper cites Boosting adversarial attacks with momentum.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Boosting adversarial attacks with momentum

Reference 6

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source=pdf_text observed=2026-08-07T12:01:36.366742Z digest=sha256:33f6be6a4af2b26c1c3389a05e898440a21329a065961b26eaa3e02862504345

Observation 836badbc-aafc-45b1-9395-6388815706a1 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Scaling rectified flow transformers for high-resolution image synthesis

Reference 7

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source=pdf_text observed=2026-08-07T12:01:36.471042Z digest=sha256:e8ba011922d0c750d961781c128bd7f195ddd50c54d86fc25b88d6fec36403c7

Observation 0aae2a1a-2309-46c8-bbd6-3ea30c53ffa7 · outbound

This paper cites Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020

Reference 8

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source=pdf_text observed=2026-08-07T12:01:36.552614Z digest=sha256:b5c3eca92c2304facdf29c6cb54d057dc4edd0103a0d37b3b11c255868aaf6bf

Observation 6ce2461a-f0f5-4dde-bc4e-8f743ef69a09 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Explaining and Harnessing Adversarial Examples

Reference 9

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source=pdf_text observed=2026-08-07T12:01:36.619295Z digest=sha256:ced1c098922a7e1ddf194ffc086f74178131d241217f6cc33161f3dc4297a82b

Observation 4e8a534d-efe7-4e0d-94ed-911d1b3b27f6 · outbound

This paper cites The Llama 3 Herd of Models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection The Llama 3 Herd of Models

Reference 10

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source=pdf_text observed=2026-08-07T12:01:36.702137Z digest=sha256:865607c45c4768edbe87315c6c5238132744acaee17a347e51fdaacf4ca0e23a

Observation 701fe434-426e-46fb-95e6-f95651ba4626 · outbound

This paper cites Deep residual learning for image recognition.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Deep residual learning for image recognition

Reference 11

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source=pdf_text observed=2026-08-07T12:01:36.764451Z digest=sha256:8c71da628cad42ce7958bbda8513c183b6b0881729f749c352cade4f1e5f9933

Observation df7d480e-a044-4a78-b18e-9da11b00b04d · outbound

This paper cites Deep residual learning for image recognition.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Deep residual learning for image recognition

Reference 12

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raw_fallback, observed 2026-08-07T12:01:40.720692Z

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.

source=pdf_text observed=2026-08-07T12:01:36.845208Z digest=sha256:32a285c6111265128eaf7b9eedb4d9ca43e0cd6278a549bc234aa45b08b06dac

Observation 2ac0e4a3-1be5-48e4-a5d4-18b4f53ac443 · outbound

This paper cites RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection

Reference 13

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source=pdf_text observed=2026-08-07T12:01:36.949688Z digest=sha256:0f823624f5237f7d4d1b8753c2b6ae76e205003374499a6a312bf0ca2cb9dc14

Observation 70ee231a-9c15-47ea-8691-9ec834d34445 · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 14

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source=pdf_text observed=2026-08-07T12:01:37.031584Z digest=sha256:9ab01d2c52d7adacad107a9a916e6f56a7096c73bc8a9f3100da8d05af79afb9

Observation 969f1500-47d4-4170-a9c0-5bf8773b40ca · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 15

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source=pdf_text observed=2026-08-07T12:01:37.122120Z digest=sha256:bb5bd4d18ead12fa4c6a324faabc8c804bca6ae77a9f2d46eb62fd14a46c0d6c

Observation 36cfd944-e977-4d75-877b-2f5ae0bf2c95 · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux, 2024.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Flux.https://github.com/black-forest-labs/flux, 2024

Reference 16

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source=pdf_text observed=2026-08-07T12:01:37.198289Z digest=sha256:c627c4667fa552e60da1e934eac30f97e05ce5f1cb91e5ae5a53d4dee9e6df39

Observation 529df50c-f4a7-49a3-8e01-b5e9c5bc1968 · outbound

This paper cites Spatial-phase shallow learning: rethinking face forgery detection in frequency domain.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Spatial-phase shallow learning: rethinking face forgery detection in frequency domain

Reference 17

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

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Observation d3b5650f-a58b-496a-8438-3021178842d7 · outbound

This paper cites Global texture enhancement for fake face detection in the wild.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Global texture enhancement for fake face detection in the wild

Reference 18

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Observation 70f0a367-aaf8-4d07-b72a-c47b95e5c571 · outbound

This paper cites Generalizing face forgery detection with high- frequency features.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Generalizing face forgery detection with high- frequency features

Reference 19

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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.

source=pdf_text observed=2026-08-07T12:01:37.455985Z digest=sha256:fa78a526a1a3e537d3b18bc00dbb63bf10125dd852eb7f9e14d7264272e92ea5

Observation efe6ce96-bd0a-4361-96b5-93d427ed8a59 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 20

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source=pdf_text observed=2026-08-07T12:01:37.529760Z digest=sha256:151b212c11e80db792cd033ddbfcf704f9732f5cf0896169a9d3d6144b93bad7

Observation 7e99bd0a-fe3d-4da3-9ec3-9635758f9cee · outbound

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

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 21

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source=pdf_text observed=2026-08-07T12:01:37.647883Z digest=sha256:1ce44b06242bf9277d368b81780d10463a56b8e83eaa4283548c742e5279bba1

Observation 68f2545c-d8f4-4118-b881-2f415bbf12bd · outbound

This paper cites Towards universal fake image detectors that generalize across generative models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Towards universal fake image detectors that generalize across generative models

Reference 22

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source=pdf_text observed=2026-08-07T12:01:37.726662Z digest=sha256:7f398547ba44509d5ed76cff6942f5e8aa134b6b48fee10dcd6a7021ad1f3c84

Observation e8c82e59-1ea3-4dd5-97f1-43b629e766dc · outbound

This paper cites Scalable diffusion models with transformers.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Scalable diffusion models with transformers

Reference 23

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source=pdf_text observed=2026-08-07T12:01:37.801100Z digest=sha256:0f7bd0d1b1aa2462fad0a16fd26f7d5ba080a377e00c870c6f4c735d29c42ff6

Observation 7cc589c5-38f0-4bf4-8f2a-e44b19ebdffe · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 24

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source=pdf_text observed=2026-08-07T12:01:37.877237Z digest=sha256:67b321bd84b7eb0701190fc9d73c97849f5e79f597bc1f57d9557f4a1ed7999c

Observation f4b82899-0804-4f96-b61e-d1b4a9cba89f · outbound

This paper cites Thinking in frequency: Face forgery detection by mining frequency-aware clues.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Thinking in frequency: Face forgery detection by mining frequency-aware clues

Reference 25

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source=pdf_text observed=2026-08-07T12:01:37.955422Z digest=sha256:fb528e77a0471c78ef003140bdc2e0460b3e6e06ba098f6014f25a2562d3ca06

Observation 2cc5d689-974e-4100-ba55-dbb04e85ecad · outbound

This paper cites Learning transferable visual models from natural language supervision.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Learning transferable visual models from natural language supervision

Reference 26

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Observation 108e1341-c557-4faf-aa76-a360dec2a6a7 · outbound

This paper cites an unresolved cited work.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Unresolved cited work

Reference 27

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

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Observation 1e7d2782-5e7b-4d06-96b5-5297337f40e4 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection High-resolution image synthesis with latent diffusion models

Reference 28

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source=pdf_text observed=2026-08-07T12:01:38.234998Z digest=sha256:31c624c89b735b9b161abc4a906ad0be8aab9b56b18c1fbe8c100a6b1e8e60fd

Observation cf06c67d-5a4d-43d8-9b8f-6718ec049e6c · outbound

This paper cites Denoising Diffusion Implicit Models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Denoising Diffusion Implicit Models

Reference 29

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source=pdf_text observed=2026-08-07T12:01:38.271543Z digest=sha256:47e21ace68f2fc32bc2e2d0a1479eeeda919fa2df3a07a2da354b075e1d2ec79

Observation 98fa54b5-60ad-4118-81a7-e911cd24546c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Score-Based Generative Modeling through Stochastic Differential Equations

Reference 30

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source=pdf_text observed=2026-08-07T12:01:38.352814Z digest=sha256:49d4d3485b9c91727298ea1f193f8971774e50a6492fa9f5acb70ffd871633f4

Observation ce615b47-8a99-4d7a-9fb6-ba1dd516570e · outbound

This paper cites Disentangling adversarial robustness and generalization.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Disentangling adversarial robustness and generalization

Reference 31

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raw_fallback, observed 2026-08-07T12:01:40.577878Z

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.

source=pdf_text observed=2026-08-07T12:01:38.521680Z digest=sha256:cdd3cebb86d558c49ea135b402fbea0d5e27402d584355f02e079c19db4950fd

Observation e524575f-5be1-4800-981a-0214e603a0bc · outbound

This paper cites C2p-clip: Injecting category common prompt in clip to enhance generalization in deepfake detection.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection C2p-clip: Injecting category common prompt in clip to enhance generalization in deepfake detection

Reference 32

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raw_fallback, observed 2026-08-07T12:01:40.506907Z

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.

source=pdf_text observed=2026-08-07T12:01:38.663523Z digest=sha256:f26b5474e889bce05654a2bed4fbce063434ac6edfe4dbca2942861097eaabb7

Observation d4c238fb-bfe4-49f9-840a-4806fab2d6a2 · outbound

This paper cites Rethinking the up- sampling operations in cnn-based generative network for generalizable deepfake detection.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Rethinking the up- sampling operations in cnn-based generative network for generalizable deepfake detection

Reference 33

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source=pdf_text observed=2026-08-07T12:01:38.746952Z digest=sha256:702648f4ef64b88dd5fc6e2a4ec767dd04c4142ef0c515323008a8699cfd24c3

Observation 526761ce-769d-42d3-9dc2-bc95a050e7da · outbound

This paper cites Cnn-generated images are surprisingly easy to spot.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Cnn-generated images are surprisingly easy to spot

Reference 34

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source=pdf_text observed=2026-08-07T12:01:38.879858Z digest=sha256:e83924a0cdc58ff62e35c8e8332def669c4e4622cbd31a3a5a51343e38ceb887

Observation a0e89856-5520-4c2b-81ce-1742e85d5b2f · outbound

This paper cites Dire for diffusion-generated image detection.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Dire for diffusion-generated image detection

Reference 35

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source=pdf_text observed=2026-08-07T12:01:39.021775Z digest=sha256:7948c581b0080f48f912c6fb73888f426e049d3b099de78133f5f029bb2b5531

Observation d4428307-54b5-4670-b5ea-88644a137a85 · outbound

This paper cites Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models

Reference 36

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Observation 13454f96-3ac7-40f8-bb5a-4d4c0b67337e · outbound

This paper cites A Sanity Check for AI-generated Image Detection.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection A Sanity Check for AI-generated Image Detection

Reference 37

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source=pdf_text observed=2026-08-07T12:01:39.273145Z digest=sha256:e623eadcbf2c4429fadc8804640f204a594bc4b30f21d1c23e12c59d7fe8a16c

Observation 1c81ec6c-7c20-40d8-913d-6f272e58acc7 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Golden Noise for Diffusion Models: A Learning Framework

Reference 38

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no resolver link, observed 2026-08-07T12:01:39.383437Z

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source=pdf_text observed=2026-08-07T12:01:39.383437Z digest=sha256:cbd24e2e80b1aed85f937d3f92b6904148dcf5621151695477e2bd51195b662c

Observation 429c8e81-9894-4b5e-b1b9-c44669addf04 · outbound

This paper cites photo of [ImageNet La- bel].

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection photo of [ImageNet La- bel]

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-07T12:01:40.253700Z

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.

source=pdf_text observed=2026-08-07T12:01:39.467348Z digest=sha256:0baee58a4d6d56d3c11b2a5ea153257d10505c460d96dff456737427ac516f1b

Observation 6117e330-e9c5-41e5-b0a9-55582e2ddba9 · outbound

This paper cites Using reverse image search is often infeasible for highly stylized or uniquely composed generations.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection Using reverse image search is often infeasible for highly stylized or uniquely composed generations

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-07T12:01:40.172548Z

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.

source=pdf_text observed=2026-08-07T12:01:39.553108Z digest=sha256:d088bb188e3f197e342e2894ffb719783658b5a2a1371005400cc6c2aef7433b

Observation ad7f3700-d222-4f27-b8ba-c5b020864c4a · outbound

This paper cites better" initial noise vectors zT than random Gaussian samples to improve generation quality or efficiency, sometimes referred to as.

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection better" initial noise vectors zT than random Gaussian samples to improve generation quality or efficiency, sometimes referred to as

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-07T12:01:40.018792Z

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.

source=pdf_text observed=2026-08-07T12:01:39.698639Z digest=sha256:2acf021ea19109e85635e2bced97d394de6d7a863b1fe2fb7c1fb6afa523cfa5

Pith citing papers

Observation c5393e74-a93e-439c-8448-4b50b2ae6595 · inbound

How Noise Benefits AI-generated Image Detection cites this paper.

How Noise Benefits AI-generated Image Detection Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Reference 83

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verified exact
arxiv_id, observed 2026-05-17T20:55:15.186475Z

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.

source=pdf_text observed=2026-05-17T20:53:31.985118Z digest=sha256:4182b44a107d4d97837678f08704039c561caf001edd568811df8703c1e5ddcc

Observation 6e8405c1-d132-41c5-89fa-ac09529b3403 · inbound

Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models cites this paper.

Simplicity Prevails: The Emergence of Generalizable AIGI Detection in Visual Foundation Models Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Reference 35

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verified exact
arxiv_id, observed 2026-05-16T08:40:46.231096Z

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.

source=pdf_text observed=2026-05-16T08:40:09.385451Z digest=sha256:c526c5e96a83825486c585c1b2249b725f32b14e8b405bbf8fe8d21512b4ccc3

Observation 3f9de24a-aced-4d66-946c-f9eb5e8d7c22 · inbound

AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection cites this paper.

AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Reference 47

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no resolver link, observed 2026-07-13T19:51:45.257204Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:51:45.257204Z digest=sha256:cecb8295639d3c69efda447c4fd076495f278fe8be3b3edb9be3d661ca9625fe

Observation 7db9e251-8725-4afc-9d29-6b1cff75bc35 · inbound

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection cites this paper.

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

Reference 21

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no resolver link, observed 2026-08-02T01:25:15.742028Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:25:15.742028Z digest=sha256:149ffaa7803aee7f4344fabae28c9297a0551a0b8e9980aaca1cf6bff4464ebe