Paper Citation Record · LEDGER

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection

As of 24 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2412.06727.

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

pith.paper-citation-record.v1
2412.06727 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:23:26.582429Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:56:20.770366Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:56:20.984260Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy43
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f837728-cfe0-4410-aae4-aaaf4a5e36f7 · outbound

This paper cites Exploring privacy and fairness risks in sharing diffusion models: An adversarial perspective,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Exploring privacy and fairness risks in sharing diffusion models: An adversarial perspective,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.817798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f77bee99-2d54-45b2-a782-54ce95a1cfb9 · outbound

This paper cites DIRE for diffusion-generated image detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection DIRE for diffusion-generated image detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.799596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c67ca6b5-472a-49b2-803d-eed551bddf75 · outbound

This paper cites Genimage: A million-scale benchmark for detecting ai- generated image,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Genimage: A million-scale benchmark for detecting ai- generated image,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.782535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2184a2d9-5305-454f-80ba-40cb299f1b6b · outbound

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

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection DRCT: diffusion reconstruction contrastive training towards universal detection of diffusion generated images,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.763279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2884a3c-deb8-40ee-9177-40f9484af772 · outbound

This paper cites Security and privacy on generative data in aigc: A survey,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Security and privacy on generative data in aigc: A survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.746256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a41ad08d-3445-4118-bb9d-b604045134ae · outbound

This paper cites Adversarial threats to deepfake detection: A practical perspective,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Adversarial threats to deepfake detection: A practical perspective,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.727390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9a1fcd58-9483-4f57-8920-8bd81f1a34f5 · outbound

This paper cites Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.706473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d53c408b-c3d4-48f1-8e1e-7276c9a18017 · outbound

This paper cites Exploring frequency adversarial attacks for face forgery detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Exploring frequency adversarial attacks for face forgery detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.688361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bfe53a9a-4264-45ae-bb8c-00e20474dff6 · outbound

This paper cites Evading deepfake detectors via adversarial statistical consistency,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Evading deepfake detectors via adversarial statistical consistency,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.620231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 353283fe-f795-45af-9bf6-375bcd90618c · outbound

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

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Towards universal fake image detectors that generalize across generative models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.506296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3aee353b-cbd8-4803-afba-40d9614ecf9c · outbound

This paper cites Forgery-aware adaptive transformer for generalizable synthetic image detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Forgery-aware adaptive transformer for generalizable synthetic image detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.433819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4bd471d8-a4d4-4ef5-9097-cd0e0a40209b · outbound

This paper cites A convnet for the 2020s,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection A convnet for the 2020s,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.374681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ba11bd1-f614-44de-95a1-c5008c8a19d4 · outbound

This paper cites Denoising diffusion implicit models,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Denoising diffusion implicit models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.254362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c9951fef-13d3-42a5-a856-f0f335787965 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Diffusion models: A comprehensive survey of methods and applications,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.234448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2f0c4ad-1d30-4d2a-b629-eb0563b269fc · outbound

This paper cites A V A: inconspicuous at- tribute variation-based adversarial attack bypassing deepfake detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection A V A: inconspicuous at- tribute variation-based adversarial attack bypassing deepfake detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.216520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7179fd5c-58ba-49aa-a495-38bd06e11902 · outbound

This paper cites Brusleattack: a query- efficient score- based black-box sparse adversarial attack,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Brusleattack: a query- efficient score- based black-box sparse adversarial attack,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.199637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e97846e1-390c-45b7-a0da-fab52e38f370 · outbound

This paper cites Ad- versarial relighting against face recognition,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Ad- versarial relighting against face recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.133662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 33ae489b-29b8-4e98-8cd1-c9f26663f625 · outbound

This paper cites A V A: adver- sarial vignetting attack against visual recognition,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection A V A: adver- sarial vignetting attack against visual recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:28.018408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e8c1738-0310-4c51-b370-3235062825ff · outbound

This paper cites Can you spot the chameleon? adversarially camouflaging images from co-salient object detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Can you spot the chameleon? adversarially camouflaging images from co-salient object detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.961555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8cb5e117-f81c-47fa-8f3d-1e44645560c2 · outbound

This paper cites Evading watermark based detection of ai-generated content,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Evading watermark based detection of ai-generated content,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.944580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9edd0d5d-0e2b-43c3-944a-0baa08936d28 · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection A style-based generator architecture for generative adversarial networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.926432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c4acdd8-9f8e-4cef-ba88-4b741ede2aff · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Progressive growing of gans for improved quality, stability, and variation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.880034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2e1b87cf-e3d0-4c9a-a9ff-c7b79478a12b · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Large scale GAN training for high fidelity natural image synthesis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.812880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 35a91852-b0ce-40d8-ae6c-199ebdc6d364 · outbound

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

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Cnn- generated images are surprisingly easy to spot... for now,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.770526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4153cfc4-a7b5-4d29-9a14-1210033befbb · outbound

This paper cites Are GAN generated images easy to detect? A critical analysis of the state-of-the-art,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Are GAN generated images easy to detect? A critical analysis of the state-of-the-art,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.721754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f80c9e4-5406-48cd-b15d-d97e51806248 · outbound

This paper cites Constructing new backbone networks via space-frequency interactive convolution for deepfake detection,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Constructing new backbone networks via space-frequency interactive convolution for deepfake detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.705345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4a8eca9-0448-40b3-bda8-42bcbb4b2ac3 · outbound

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

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection High- resolution image synthesis with latent diffusion models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.687505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b658642-412a-47aa-87ad-219a5907eb47 · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Vector quantized diffusion model for text-to-image synthesis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.669455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b72b954a-96cf-4f0a-ace1-46224c3db2dd · outbound

This paper cites GLIDE: towards photorealistic image generation and editing with text-guided diffusion models,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection GLIDE: towards photorealistic image generation and editing with text-guided diffusion models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.581081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cc0c628a-3309-4fa0-a426-118386d1b823 · outbound

This paper cites Meta gradient adversarial attack,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Meta gradient adversarial attack,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.424048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7067e8f8-3c4a-40e5-9ede-43548bf4e7f3 · outbound

This paper cites Knowl- edge representation of training data with adversarial examples supporting decision boundary,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Knowl- edge representation of training data with adversarial examples supporting decision boundary,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.284010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb01b456-e2d3-4a6d-adcf-92a5e7e35b73 · outbound

This paper cites Square at- tack: A query-efficient black-box adversarial attack via random search,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Square at- tack: A query-efficient black-box adversarial attack via random search,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.189160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 689ccc78-e070-404b-958a-098e4240fbe3 · outbound

This paper cites Particle swarm optimization,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Particle swarm optimization,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.170435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3eb08584-df84-4fb0-afb9-223cffc12dbe · outbound

This paper cites Particle swarm optimization algorithm and its applications: a systematic review,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Particle swarm optimization algorithm and its applications: a systematic review,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.153343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f81c9e62-cee2-42c9-83cd-08971162bfee · outbound

This paper cites Optimizing latent variables in integrating transfer and query based attack framework,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Optimizing latent variables in integrating transfer and query based attack framework,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.133873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 15060768-1f43-4cbd-a991-487dd97aa5cf · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Image quality assessment: from error visibility to structural similarity,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T19:23:26.531195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43f8e5ea-daaf-4d62-8428-d9303e83b308 · outbound

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

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Imagenet: A large-scale hierarchical image database,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.102718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a1276fc-7445-42c5-af8a-27b1c8e2d1ce · outbound

This paper cites Microsoft COCO: common objects in context,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Microsoft COCO: common objects in context,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.084759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4bd30007-7ad0-42e6-9830-6917b35ea373 · outbound

This paper cites Explaining and harnessing adversarial examples,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Explaining and harnessing adversarial examples,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.050816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30bad0e6-baa9-4fb1-95e4-8ffc81c89239 · outbound

This paper cites Boosting adversarial attacks with momentum,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Boosting adversarial attacks with momentum,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.030016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c76f05d8-4498-4958-89f5-95e977f0a8ed · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Towards deep learning models resistant to adversarial attacks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:27.009515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e75177f-ad2a-46d8-b257-a19f888c71e2 · outbound

This paper cites Simple black-box adversarial attacks,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Simple black-box adversarial attacks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:26.953486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6086bf8e-b4c6-4990-a332-02e3cc223abd · outbound

This paper cites No-reference image quality assessment in the spatial domain,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection No-reference image quality assessment in the spatial domain,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:26.819636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c8729bc5-8428-469b-8fc0-a09292962a81 · outbound

This paper cites Ai-generated image identification service of image moderation 2.0,.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Ai-generated image identification service of image moderation 2.0,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:23:26.628611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9a1ad566-8b26-429c-a28b-2b240e0f57e3 · outbound

This paper cites an unresolved cited work.

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection Unresolved cited work

Reference 8693

Resolution: unresolved; no resolver link, observed 2026-08-11T19:23:26.548275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d8c8f9b3-ce7e-4c79-9da0-6f3cac1d0df0 · inbound

Fooling the Watchers: Breaking AIGC Detectors via Semantic Prompt Attacks cites this paper.

Fooling the Watchers: Breaking AIGC Detectors via Semantic Prompt Attacks Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T12:56:21.058197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.