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Paper Citation Record · LEDGER

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2505.17579.

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

pith.paper-citation-record.v1
2505.17579 v3

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:48.767310Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 92a9d4d8-bc2a-4630-8874-99076aca200f · outbound

This paper cites Deep Intellectual Property Protection: A Survey.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Deep Intellectual Property Protection: A Survey

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:47.596326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:47.596326Z digest=sha256:bfcaf327cf0930605e70c86a863e997435ebec5882f2ac53effa721ae778107f

Observation 6fd12706-7b0e-456c-b913-3035a14c5d8e · outbound

This paper cites Adversarial examples in the physical world.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Adversarial examples in the physical world

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:47.707265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:47.707265Z digest=sha256:0e49f3619bcfbb5178ca9bd7d1fa4b8bcab07e7c119d5e68c131c8eb4732daa6

Observation 01caabd8-225a-4e77-9850-26f5b313a41f · outbound

This paper cites Customized Watermarking for Deep Neural Networks via Label Distribution Perturbation.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Customized Watermarking for Deep Neural Networks via Label Distribution Perturbation

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:49:49.078445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:47.790079Z digest=sha256:e637c270ffa54307dc26ed64e156acede3e1ccb83629c8726d52bce276204a8c

Observation 4ff63cf6-297a-4007-bdc0-3f69813e07c1 · outbound

This paper cites Turning your weakness into a strength: Watermarking deep neural networks by backdooring,.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Turning your weakness into a strength: Watermarking deep neural networks by backdooring,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:49:50.163826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:47.899982Z digest=sha256:14120b748b71c105a34dfc7027925a7faa2524c964e61640134d9e67a8577f9d

Observation 6ef72ba0-d42a-47d5-82ba-db22dce64420 · outbound

This paper cites AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:47.975982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:47.975982Z digest=sha256:f983ee8a08d8c5b884b2b02c8cb9ae5594a3b264d72f64b9171ecc77648ab1c2

Observation 31f05c83-a9b5-4f5b-97d8-5fd97f02afac · outbound

This paper cites SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:48.056361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:48.056361Z digest=sha256:b033891ddcc607136244ab1ed75b39db15625f4c52c58bb51e607ff565f0013e

Observation 5f1e14f7-a77a-4e3e-af38-d40ce50294c2 · outbound

This paper cites IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:48.145267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:48.145267Z digest=sha256:e50c8386a3c4caf98c4b46fbf2a2f3404ea8d9d3aadcdc2c5531efbb60c64f0b

Observation 5c4dadde-51c4-4e42-b275-30a1f844a2cd · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Explaining and Harnessing Adversarial Examples

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:48.240706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:48.240706Z digest=sha256:0e8e2bf28216d0b42d0297c1714e38567a68004764e3c1ad25f6474e84267355

Observation 7d8f2a72-b4c2-40fd-a5b1-dfffd21511d2 · outbound

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

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Imagenet: A large-scale hierarchical image database,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:49:49.974792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:48.323491Z digest=sha256:46e192d567ee0f6919f2bd796ee32bbc5fbf46635aef6dc0329e2e0ed37dde93

Observation ac6b6620-32a0-4d65-859a-a5b05000009b · outbound

This paper cites Deep residual learning for image recognition,.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Deep residual learning for image recognition,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:48.412336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:48.412336Z digest=sha256:96d04af3e00fbb2848bbee0d3e141d12070597f207c735a0c5dac2efcf235c8a

Observation 043e974b-9e07-4088-b90a-98ace881b77e · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Pytorch: An imperative style, high-performance deep learning library,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:49:49.819612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:48.477252Z digest=sha256:b6867f3d10d284b89ff036b171859ca1334c008cba80ab833d87e9871c47762d

Observation 571b673c-05e6-4070-a9ca-3d4bfdcd83d8 · outbound

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

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Image quality assessment: From error visibility to structural similarity,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:49:49.692500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:48.583138Z digest=sha256:b81ca42f5f07288c26e8630379250b9590b601c15fcb757a607c36be12966e8b

Observation 58ed454f-22e5-4624-aed4-baa53c1a9787 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:48.692444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:48.692444Z digest=sha256:ef6e86ce479c4bf3957e43d5c843e567c4d48eed98244eff99d87ee27cddc19d

Observation 5e122db0-c38a-414f-a253-0fdda2d7a59d · outbound

This paper cites ADV oIP: Adver- sarial detection of encrypted and concealed voip,.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation ADV oIP: Adver- sarial detection of encrypted and concealed voip,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:49:49.526734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:48.767310Z digest=sha256:650125e0af19d5d3eb20400bd834123d6a914517a7e78745a8e480d7046e1e38

Observation 8e14c429-f86b-48d2-ab0e-3b9477076ef2 · outbound

This paper cites A survey of deep neural network watermarking techniques.

Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation A survey of deep neural network watermarking techniques

Reference 2021

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T14:49:49.343823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:49:47.525966Z digest=sha256:ff42a3f0d51c1902e27d1a62eca8430a1439befeace048d2f402e06b903b4986

Pith citing papers

No inbound Pith citation observations are available.