Paper Citation Record · LEDGER

Active Adversarial Noise Suppression for Image Forgery Localization

As of 14 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2506.12871.

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

pith.paper-citation-record.v1
2506.12871 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:41:18.538038Z

measured 56 of 56 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:10:02.825455Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:10:03.376594Z

Reference resolution

55 of 55 outbound references displayed

verified exact0
verified fuzzy41
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 95312af8-863c-4e86-8bd5-6658b230805b · outbound

This paper cites Deep matching and validation network: An end-to-end solution to constrained image splicing localization and detection,.

Active Adversarial Noise Suppression for Image Forgery Localization Deep matching and validation network: An end-to-end solution to constrained image splicing localization and detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.211216Z

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 256bc1f5-597f-4ee0-8f7e-2c37f7eb37a0 · outbound

This paper cites Color noise-based fea- ture for splicing detection and localization,.

Active Adversarial Noise Suppression for Image Forgery Localization Color noise-based fea- ture for splicing detection and localization,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.198609Z

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 28e45a16-7181-41bd-9fdb-125767e98e0a · outbound

This paper cites Multi- task SE-network for image splicing localization,.

Active Adversarial Noise Suppression for Image Forgery Localization Multi- task SE-network for image splicing localization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.186991Z

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 22661efe-e1b2-4f84-aea5-6b771da7e0c7 · outbound

This paper cites BusterNet: Detecting copy-move image forgery with source/target localization,.

Active Adversarial Noise Suppression for Image Forgery Localization BusterNet: Detecting copy-move image forgery with source/target localization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.175418Z

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 cfcbe5db-07f5-4895-a8cc-6f541bb2a3b3 · outbound

This paper cites DOA-GAN: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization,.

Active Adversarial Noise Suppression for Image Forgery Localization DOA-GAN: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.163863Z

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 9607307b-ece1-4316-b45a-4e3300ff2ee4 · outbound

This paper cites A deep learning approach to patch-based image inpainting forensics,.

Active Adversarial Noise Suppression for Image Forgery Localization A deep learning approach to patch-based image inpainting forensics,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.148790Z

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 2f2428de-0e66-4333-8ff1-41c6a292698d · outbound

This paper cites Spatiotemporal trident networks: detection and localization of object removal tampering in video passive forensics,.

Active Adversarial Noise Suppression for Image Forgery Localization Spatiotemporal trident networks: detection and localization of object removal tampering in video passive forensics,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.136774Z

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 5897a786-a49e-4d8e-bdfa-1effe8e08213 · outbound

This paper cites PSCC-Net: Progressive spatio- channel correlation network for image manipulation detection and localization,.

Active Adversarial Noise Suppression for Image Forgery Localization PSCC-Net: Progressive spatio- channel correlation network for image manipulation detection and localization,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.124555Z

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 14220351-027d-44cd-bd2b-3fb43e96ef0a · outbound

This paper cites Learning JPEG compression artifacts for image manipulation detection and lo- calization,.

Active Adversarial Noise Suppression for Image Forgery Localization Learning JPEG compression artifacts for image manipulation detection and lo- calization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.112193Z

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 cb7fa2e8-fd6a-4bed-bba4-a868a1af1a68 · outbound

This paper cites MVSS-Net: Multi- view multi-scale supervised networks for image manipulation detection,.

Active Adversarial Noise Suppression for Image Forgery Localization MVSS-Net: Multi- view multi-scale supervised networks for image manipulation detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.097916Z

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 76789907-b2eb-4a81-8c93-83617a462986 · outbound

This paper cites Robust image forgery detection against transmission over online social networks,.

Active Adversarial Noise Suppression for Image Forgery Localization Robust image forgery detection against transmission over online social networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.078980Z

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 258aaefc-cf10-4fd4-8945-e7d0f6fb5d5a · outbound

This paper cites Employing reinforcement learning to construct a decision-making environment for image forgery localization,.

Active Adversarial Noise Suppression for Image Forgery Localization Employing reinforcement learning to construct a decision-making environment for image forgery localization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.066386Z

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 7f87c102-e80e-440a-9f8b-a4fd598a9cf6 · outbound

This paper cites HDF-Net: Capturing homogeny difference features to localize the tampered image,.

Active Adversarial Noise Suppression for Image Forgery Localization HDF-Net: Capturing homogeny difference features to localize the tampered image,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.054316Z

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 a125fa41-2035-4c48-b92d-ac9f863938d7 · outbound

This paper cites Poster: Query-efficient black- box attack for image forgery localization via reinforcement learning,.

Active Adversarial Noise Suppression for Image Forgery Localization Poster: Query-efficient black- box attack for image forgery localization via reinforcement learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.042141Z

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 ca39ca0d-b113-4224-a603-877f0a06b39f · outbound

This paper cites Query-efficient attack for black-box image inpainting forensics via reinforcement learning,.

Active Adversarial Noise Suppression for Image Forgery Localization Query-efficient attack for black-box image inpainting forensics via reinforcement learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.026378Z

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 f7dddc9f-adac-450a-849f-9eedceefc6ca · outbound

This paper cites How deep learning sees the world: A survey on adversarial attacks & defenses,.

Active Adversarial Noise Suppression for Image Forgery Localization How deep learning sees the world: A survey on adversarial attacks & defenses,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:15.693646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d334c1-c3d5-474b-8713-013419b734fd · outbound

This paper cites Image tampering local- ization using a dense fully convolutional network,.

Active Adversarial Noise Suppression for Image Forgery Localization Image tampering local- ization using a dense fully convolutional network,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:19.006331Z

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 1fde3ee2-7c82-4222-a250-0824daa9bd90 · outbound

This paper cites Deep residual learning for image recognition,.

Active Adversarial Noise Suppression for Image Forgery Localization Deep residual learning for image recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:15.931397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d9305bd-86a9-4c5e-b23c-85cab8e95af1 · outbound

This paper cites Recalibrating fully convo- lutional networks with spatial and channel “squeeze and excitation.

Active Adversarial Noise Suppression for Image Forgery Localization Recalibrating fully convo- lutional networks with spatial and channel “squeeze and excitation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.984781Z

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 cba03204-e814-4891-96c5-0eca90669680 · outbound

This paper cites U-Net: Convolutional net- works for biomedical image segmentation,.

Active Adversarial Noise Suppression for Image Forgery Localization U-Net: Convolutional net- works for biomedical image segmentation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:16.156525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b1ca2ef-6a22-4a8c-a797-4ce94258aafb · outbound

This paper cites an unresolved cited work.

Active Adversarial Noise Suppression for Image Forgery Localization Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:16.273690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4bd5c78-df39-4f1f-9f3a-59a4d48ae072 · outbound

This paper cites Asynchronous methods for deep rein- forcement learning,.

Active Adversarial Noise Suppression for Image Forgery Localization Asynchronous methods for deep rein- forcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.958632Z

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 78cfa177-5050-4fe5-9546-919215ba3bea · outbound

This paper cites Rich models for steganalysis of digital images,.

Active Adversarial Noise Suppression for Image Forgery Localization Rich models for steganalysis of digital images,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.946288Z

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 ed796d30-9911-4203-96fd-5994c80726e7 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Active Adversarial Noise Suppression for Image Forgery Localization Explaining and Harnessing Adversarial Examples

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:16.598518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 182a5f34-a348-4e54-a2c4-c233b378432b · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Active Adversarial Noise Suppression for Image Forgery Localization Towards evaluating the robustness of neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.934934Z

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 4939ed2d-ec77-44f3-838a-b0eefbcc9072 · outbound

This paper cites Adversarial examples in the physical world,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial examples in the physical world,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.921927Z

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 d879d106-cf73-4292-9a04-619e0568c339 · outbound

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

Active Adversarial Noise Suppression for Image Forgery Localization Towards deep learning models resistant to adversarial attacks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:16.907182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6aaa153-3799-47e4-88f9-dd4d4c4f7e0f · outbound

This paper cites Adversarial attacks for image segmentation on multiple lightweight models,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial attacks for image segmentation on multiple lightweight models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.902195Z

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 e04ad9a8-3bb9-4034-90bb-9a7798c7eb2c · outbound

This paper cites Adversarial attacks on yolact instance segmentation,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial attacks on yolact instance segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.889372Z

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 7f305f21-238b-4f27-878a-110508953cff · outbound

This paper cites Proximal splitting adversarial attack for semantic segmentation,.

Active Adversarial Noise Suppression for Image Forgery Localization Proximal splitting adversarial attack for semantic segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.877413Z

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 40db1624-a03e-463a-8bec-1ae462998da8 · outbound

This paper cites Universal adversarial perturbations against object detection,.

Active Adversarial Noise Suppression for Image Forgery Localization Universal adversarial perturbations against object detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.866056Z

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 1fedd711-daf5-4889-b757-7aaf107d667d · outbound

This paper cites Adc: Adversarial attacks against object detection that evade context consistency checks,.

Active Adversarial Noise Suppression for Image Forgery Localization Adc: Adversarial attacks against object detection that evade context consistency checks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.854588Z

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 e6c510f8-84d6-4691-9e00-c9d500bcc8ba · outbound

This paper cites Adversarial patch attacks against aerial imagery object detectors,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial patch attacks against aerial imagery object detectors,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.843076Z

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 90761e03-04fd-441c-ba06-d5be5c1fc33e · outbound

This paper cites Adversarial risk and the dangers of evaluating against weak attacks,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial risk and the dangers of evaluating against weak attacks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.831517Z

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 3d7eca04-afb4-43e8-82a1-5d88791c3806 · outbound

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

Active Adversarial Noise Suppression for Image Forgery Localization Square attack: a query-efficient black-box adversarial attack via random search,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.818975Z

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 72564420-9403-4557-9368-ea9097b926f1 · outbound

This paper cites Boosting adversarial attacks with momentum,.

Active Adversarial Noise Suppression for Image Forgery Localization Boosting adversarial attacks with momentum,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.805757Z

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 161f27d2-8188-4b3a-950d-346654f3d428 · outbound

This paper cites Boosting adversarial transferability by achieving flat local maxima,.

Active Adversarial Noise Suppression for Image Forgery Localization Boosting adversarial transferability by achieving flat local maxima,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.791749Z

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 2af3b43a-ae9f-4099-8cba-ff6a3513d31f · outbound

This paper cites Robustness may be at odds with accuracy,.

Active Adversarial Noise Suppression for Image Forgery Localization Robustness may be at odds with accuracy,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.777853Z

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 53913b7f-002c-41ab-a552-281836a3e571 · outbound

This paper cites A study of the effect of JPG compression on adversarial images.

Active Adversarial Noise Suppression for Image Forgery Localization A study of the effect of JPG compression on adversarial images

Reference 39

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Source-reported events for the cited work

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Observation 878bbdf4-0eae-44f5-b6ef-94693dd0596d · outbound

This paper cites Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression.

Active Adversarial Noise Suppression for Image Forgery Localization Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:17.872611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70cc03b6-4265-4918-bf5d-afc2fff235a7 · outbound

This paper cites Adversarial examples for semantic segmentation and object detection,.

Active Adversarial Noise Suppression for Image Forgery Localization Adversarial examples for semantic segmentation and object detection,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.762195Z

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 46e53a34-3e27-455c-93b3-b0bb41e27ad4 · outbound

This paper cites Defense-gan: Protect- ing classifiers against adversarial attacks using generative models,.

Active Adversarial Noise Suppression for Image Forgery Localization Defense-gan: Protect- ing classifiers against adversarial attacks using generative models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.748890Z

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 4bee0f04-7295-4ea9-94b8-db6e43c3721c · outbound

This paper cites Collaborative defense- gan for protecting adversarial attacks on classification system,.

Active Adversarial Noise Suppression for Image Forgery Localization Collaborative defense- gan for protecting adversarial attacks on classification system,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.735836Z

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 662472bf-924d-4b0d-b8ad-f5f7a20f3466 · outbound

This paper cites Guided Diffusion Model for Adversarial Purification.

Active Adversarial Noise Suppression for Image Forgery Localization Guided Diffusion Model for Adversarial Purification

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.003374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5717ff7-fea7-4154-a267-ca2c55ade2ca · outbound

This paper cites Denoising diffusion probabilistic models,.

Active Adversarial Noise Suppression for Image Forgery Localization Denoising diffusion probabilistic models,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.723042Z

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 062a49eb-9e60-4b27-bcd5-46154d69feb9 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Active Adversarial Noise Suppression for Image Forgery Localization UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.175132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f95c36b-b830-45ff-b0ed-004f1bddaab7 · outbound

This paper cites EfficientNet: Rethinking model scaling for convolu- tional neural networks,.

Active Adversarial Noise Suppression for Image Forgery Localization EfficientNet: Rethinking model scaling for convolu- tional neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.709216Z

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 e196b505-fea1-4dea-90e7-cca171a15905 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Active Adversarial Noise Suppression for Image Forgery Localization ImageNet Large Scale Visual Recognition Challenge,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.386221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a957ca8e-fd32-4316-b9a5-fd9597d913a6 · outbound

This paper cites Visualizing and understanding convo- lutional networks,.

Active Adversarial Noise Suppression for Image Forgery Localization Visualizing and understanding convo- lutional networks,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.441859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b78b48-55cf-4463-ab10-26008622af87 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

Active Adversarial Noise Suppression for Image Forgery Localization V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.505754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7325920-aa42-4bab-b41c-4b5ffb5ee5ee · outbound

This paper cites A data set of authentic and spliced image blocks,.

Active Adversarial Noise Suppression for Image Forgery Localization A data set of authentic and spliced image blocks,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.674311Z

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 98db6fb9-9931-4be1-b044-d348890ddc56 · outbound

This paper cites Casia image tampering detection eval- uation database,.

Active Adversarial Noise Suppression for Image Forgery Localization Casia image tampering detection eval- uation database,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.660714Z

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 655b1fb0-fd53-4b24-a0de-547f5545d6cb · outbound

This paper cites IMD2020: a large-scale annotated dataset tailored for detecting manipulated images,.

Active Adversarial Noise Suppression for Image Forgery Localization IMD2020: a large-scale annotated dataset tailored for detecting manipulated images,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.647982Z

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 8c505793-6bbc-414a-a014-ee5ddcb46bb2 · outbound

This paper cites Multiple image splicing dataset (MISD): a dataset for multiple splicing,.

Active Adversarial Noise Suppression for Image Forgery Localization Multiple image splicing dataset (MISD): a dataset for multiple splicing,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:18.634766Z

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 f566cb70-2650-46e3-86d6-d350de87e4be · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Active Adversarial Noise Suppression for Image Forgery Localization Adam: A Method for Stochastic Optimization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:18.538038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6c680580-b1eb-411f-a497-98127d1a68b5 · inbound

ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack cites this paper.

ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack Active Adversarial Noise Suppression for Image Forgery Localization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:10:03.385031Z

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.