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Paper Citation Record · LEDGER

Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detection

As of 13 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 2026-08-13T06:32:02.005865+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
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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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raw_fallback, observed 2026-08-07T12:01:40.821243Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:35.951536Z digest=sha256:b84bc9399b4d105b01f23293cc12fde19be5f8be745f8f42e138f32e3c66762f

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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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:36.126998Z digest=sha256:f28a5cf263b59c25b0726ebfc56e9aba63cf1daa0d57af56faf3a7b714a2b790

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:f664a5497321060b350ab345c77c1be50d8c2769f854668763de9610f7d2bf6e

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:f046e925d5195ce10734d64e0322cd5978269329c18c54f41ede0847c43a3eab

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=sha256:6219cb76562a74c6794ec15f5784df653491b052583a19c3415ba36c9696be94

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=sha256:91ca020d9ec37812932a58a2e6eeac0c5bb7b84e4f6b712f719821a68d99e312

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:f4dfabefa8ea4fa0cac593cb7e720c835b2dbd46d9369b6245dff5e9abfdcd41

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=sha256:494363356ded3be139793d744e3f081ca785a65e3cc61ea90ace9b88d33cd69d

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=sha256:65463f20aee42fb8d8f65de855f303e1e16747705d4c18cc8947cc41abc46b12

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=sha256:8d6e1b01b6865a57c1110c4b5b4cdd55ab2475b674a0328e867bf6d0f5421966

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:36.845208Z digest=sha256:740c16c3dafefd66c719d919ca0c39ff8de908f9def4bcb5cdb4bfa7b2fc75a4

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=sha256:320390f73ce7a7d77777ca7175502e96d3c9e92ac0cd036f06c9db1796c06cdf

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=sha256:5767e479343dbe613002c3b8b0d7ca3832d002982bd08bd9e971796be71e3d49

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=sha256:08f1a267ad18a246f0249785e30667bf25ef8ab2eca46d37c83c3bda563dd239

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:c09fa076fd55a7901cb454ada811945d5077304fc8e236b4b05fb3c7a73e7cf9

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:37.281494Z digest=sha256:4ecd271ac47e6a3440c1fcef0432399cebebf4e1eb868d9df59810e7a7ddb63a

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:37.455985Z digest=sha256:4095b6077a3934d2efc3e2d35cb6fa9084baa55e6f504b666596b24c5c1278fc

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:e2905486644086ddb09eecb31dd7364169d1b20f722e22149991c8aa33065c4b

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:a608f85e6f429eee5b1552e7aa690824089e558ee09497d84e91077990bb5778

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=sha256:675f49b1761b82ceaeba926aa899d0e87a5c48b7747b557dd4177207419d5c37

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:cc8e1fb12c6c28c78e6c0d41101db7d0636e13e0e2d14d9205ede1ecffd05bac

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=sha256:0976b346eee27f22eea919db134eed90aa58e5f8c1b499a9d18a3aec51376b05

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=sha256:1ab39afe7d81b73dcf6107ad751bad4c7ebce7e5e79e4270fc4c1698876e370e

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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source=pdf_text observed=2026-08-07T12:01:38.052908Z digest=sha256:655b71dda78a73d838dd0fcb2e2a0dba9c21efb6f529ad7903e27d744ef86fb0

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:38.134684Z digest=sha256:aa745368c66212533c5cf24b82117cbd96149b1bf2e4b2588f6332d8623da2bd

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:f02bce5a2342afb6be6913c958a5d668157e3e722e1bfc9f14f784dca01def9b

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=sha256:3130c0bc472f424eea1653022448296de3fc6039b1c2c27bfbc77ad8bb88142c

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=sha256:11deb25a4977a02cbbc6474d658d3ff094076419401f199025d28b80939abadc

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:38.521680Z digest=sha256:b8d8a3b3968175e04836edc20d8f96bb74c62279fb084d61d7700a1881ef41f0

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:38.663523Z digest=sha256:de211bbad35865a3215c010c46ebdb3c56ad2c9bec9522e787425cd3b05b40f3

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=sha256:3ea3581b85669ce7216dc5aa1c4144c6b74009f4783aa624aa8e88f19362c748

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:cc3e6dd50142c498403ded28e538e87e3943cdf384fa48b13f69b17538361368

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:e7cacddee24f00fd6768cb666e6b3b3de7f75d1856d927807408fea001d01e01

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:e990cb18df89c8cd7ba5c88ec7e59f93d27208715814249e615ae519d12766bc

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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unresolved
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=sha256:533657974f3231980c893b4496d40851401126292c96b7fb3606b0fea0dce6ac

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:39.467348Z digest=sha256:792cf6e3ef6731b58922958cf27bd1e1af3c98dd517c377297089151bc18aa35

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:39.553108Z digest=sha256:69e6f228b7792cf0254e6d5a272fdab52366aee573ab16941d0c597a86d9da96

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T12:01:39.698639Z digest=sha256:f37b1ea28050c7b6de266192b0cc5df5caa3539f8c86f940c022a98d85bab811

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-17T20:53:31.985118Z digest=sha256:ea5e65a263987612c9b9d994030d93490eba88d9b48cf47444e06d0d2bc98f67

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-16T08:40:09.385451Z digest=sha256:139f498f5d35140ff715349fe9352d6bdc4f385f33f5c5ee4ba9498542ae8772

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:51:45.257204Z digest=sha256:9e35394c5d12d3c3fd7274a8d16413d65dbcdf9088bb9249ba021e1bd1e73362

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:25:15.742028Z digest=sha256:8823b0ca1fd0614161f41ad851cd2eeada16b98142a8ad46f9500768cf4859ff