Paper Citation Record · LEDGER

Explore the vulnerability of black-box models via diffusion models

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2506.07590.

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

pith.paper-citation-record.v1
2506.07590 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:38:26.388096Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:38:22.211976Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:38:26.772211Z

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy20
unresolved11
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 882318f0-82e7-487b-b17a-73d6027f28f5 · outbound

This paper cites Explore the vulnerability of black-box models via diffusion models.

Explore the vulnerability of black-box models via diffusion models Explore the vulnerability of black-box models via diffusion models

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:26.873879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 649b364b-7a2e-49a5-beb1-552e0b7a4ae3 · outbound

This paper cites Diffusion-based generative models, known for generating high-fidelity and diverse synthetic images, are central to our research, particularly stable and latent diffu- sion models.

Explore the vulnerability of black-box models via diffusion models Diffusion-based generative models, known for generating high-fidelity and diverse synthetic images, are central to our research, particularly stable and latent diffu- sion models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:30.593333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a187c80-e236-4878-a400-8cdf3ef27217 · outbound

This paper cites an unresolved cited work.

Explore the vulnerability of black-box models via diffusion models Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64ba7d80-bd91-4bf7-81b2-3d3fd8150215 · outbound

This paper cites Experiments on Model Extraction 4.1.1.

Explore the vulnerability of black-box models via diffusion models Experiments on Model Extraction 4.1.1

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 910d23f9-8830-457f-9d6d-4faddd0d5da1 · outbound

This paper cites We developed a method for training robust substitute models in a data-free, hard-label, and query-limited setting.

Explore the vulnerability of black-box models via diffusion models We developed a method for training robust substitute models in a data-free, hard-label, and query-limited setting

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:30.139455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6dab6e1-be3e-4406-a62b-19105cc62fb4 · outbound

This paper cites Data-free model extraction,.

Explore the vulnerability of black-box models via diffusion models Data-free model extraction,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:30.025492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08a9ed2c-5478-49b8-b443-c07aeb598d66 · outbound

This paper cites Disguide: Disagreement-guided data-free model extraction,.

Explore the vulnerability of black-box models via diffusion models Disguide: Disagreement-guided data-free model extraction,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.864264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ac29e3b-a42e-4666-b704-8a6efd7ee427 · outbound

This paper cites Dast: Data-free substitute training for adversarial attacks,.

Explore the vulnerability of black-box models via diffusion models Dast: Data-free substitute training for adversarial attacks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.641566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e647d121-605b-4bb8-b8a5-0c3adf9d492f · outbound

This paper cites Towards efficient data free black-box adversarial attack,.

Explore the vulnerability of black-box models via diffusion models Towards efficient data free black-box adversarial attack,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.385289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36ab0e62-3d0d-45e4-86a1-1ecab2e19016 · outbound

This paper cites Why do adversarial attacks transfer? explaining transferability of evasion and poi- soning attacks,.

Explore the vulnerability of black-box models via diffusion models Why do adversarial attacks transfer? explaining transferability of evasion and poi- soning attacks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.114300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b7426f4-2718-48a5-8935-7801f565e588 · outbound

This paper cites Catastrophic forgetting and mode collapse in gans,.

Explore the vulnerability of black-box models via diffusion models Catastrophic forgetting and mode collapse in gans,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.935621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d5ef2ec-41fa-4945-9e7a-7165f50d264a · outbound

This paper cites Adversarial attacks against deep generative models on data: a survey,.

Explore the vulnerability of black-box models via diffusion models Adversarial attacks against deep generative models on data: a survey,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.769909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 14f453da-b792-461c-b6a7-3a7cbe320d16 · outbound

This paper cites Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks.

Explore the vulnerability of black-box models via diffusion models Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:23.487864Z

Source-reported events for the cited work

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Observation dbd470d5-fa12-4c62-85b3-0b11381d7ce5 · outbound

This paper cites Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models,.

Explore the vulnerability of black-box models via diffusion models Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.595281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6610efc6-2005-4416-9f0a-57a87543b988 · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Explore the vulnerability of black-box models via diffusion models FitNets: Hints for Thin Deep Nets

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8096c855-ecd7-4623-9ba5-3d2fcc001090 · outbound

This paper cites Maze: Data-free model stealing attack us- ing zeroth-order gradient estimation,.

Explore the vulnerability of black-box models via diffusion models Maze: Data-free model stealing attack us- ing zeroth-order gradient estimation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.422319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7a929063-12d1-4fd7-81f8-8fc0c0b1b294 · outbound

This paper cites Zero-shot knowledge distillation from a decision-based black-box model,.

Explore the vulnerability of black-box models via diffusion models Zero-shot knowledge distillation from a decision-based black-box model,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.255340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ab7e70dc-919c-4b98-ba77-8086afc5aa22 · outbound

This paper cites Towards data-free model stealing in a hard label setting,.

Explore the vulnerability of black-box models via diffusion models Towards data-free model stealing in a hard label setting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.131392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c20387de-4b05-487d-b6b8-7bce4d46e30e · outbound

This paper cites VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces.

Explore the vulnerability of black-box models via diffusion models VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:38:26.641223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57792c09-29c6-45c6-b425-7d71c879b4ed · outbound

This paper cites Dualcos: Query-efficient data-free model stealing with dual clone networks and optimal samples,.

Explore the vulnerability of black-box models via diffusion models Dualcos: Query-efficient data-free model stealing with dual clone networks and optimal samples,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.961925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ef28d2e9-8c6c-4151-b0e3-149b185e49a4 · outbound

This paper cites Practical black-box attacks against machine learning,.

Explore the vulnerability of black-box models via diffusion models Practical black-box attacks against machine learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.815604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65cf8257-2aa7-4436-afc2-c9726fc03586 · outbound

This paper cites Knockoff nets: Stealing functionality of black-box models,.

Explore the vulnerability of black-box models via diffusion models Knockoff nets: Stealing functionality of black-box models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.706205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0f7991d-9f79-417d-83b3-df088ebd90c3 · outbound

This paper cites Difattack: Query-efficient black-box adversarial at- tack via disentangled feature space,.

Explore the vulnerability of black-box models via diffusion models Difattack: Query-efficient black-box adversarial at- tack via disentangled feature space,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.509082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6da76ab3-d966-45a9-8e42-8ff2e9aa64bb · outbound

This paper cites Derd: data-free adversarial robustness distillation through self-adversarial teacher group,.

Explore the vulnerability of black-box models via diffusion models Derd: data-free adversarial robustness distillation through self-adversarial teacher group,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.318189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da556fe9-b69b-4064-a3e7-1baf51fbf98f · outbound

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

Explore the vulnerability of black-box models via diffusion models High-resolution im- age synthesis with latent diffusion models,

Reference 25

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Source-reported events for the cited work

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Observation 9963415e-0095-4cb8-881f-49b92198ba0e · outbound

This paper cites Learning multiple layers of features from tiny images,.

Explore the vulnerability of black-box models via diffusion models Learning multiple layers of features from tiny images,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.329071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 417ba244-8cf4-4ad3-9e85-28428d40ddf9 · outbound

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

Explore the vulnerability of black-box models via diffusion models Imagenet: A large-scale hierarchical image database,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.189788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97474fa2-6302-430d-8ca8-3e75a3effc42 · outbound

This paper cites Tiny imagenet visual recogni- tion challenge,.

Explore the vulnerability of black-box models via diffusion models Tiny imagenet visual recogni- tion challenge,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:27.021754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5d935f33-a1ef-40ef-a6ed-f743c50ae927 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Explore the vulnerability of black-box models via diffusion models Imagenet classification with deep convolutional neural networks,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.847394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e23e900-33fd-4eea-a895-ebaab0b0a1f5 · outbound

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

Explore the vulnerability of black-box models via diffusion models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.012423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a2183a4-28a7-4254-953f-451730f850f5 · outbound

This paper cites Wide Residual Networks.

Explore the vulnerability of black-box models via diffusion models Wide Residual Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.099644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37fd0a82-69d7-442a-b0ee-b0b868b6ff15 · outbound

This paper cites Deep residual learning for image recognition,.

Explore the vulnerability of black-box models via diffusion models Deep residual learning for image recognition,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.164974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b72b8892-a0b5-4fbf-aa6e-ae822a17f48c · outbound

This paper cites Adversarial Machine Learning at Scale.

Explore the vulnerability of black-box models via diffusion models Adversarial Machine Learning at Scale

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.232722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36a7b84a-aa7e-47e3-b4bd-26a76646ee5a · outbound

This paper cites Adversarial examples in the physical world.

Explore the vulnerability of black-box models via diffusion models Adversarial examples in the physical world

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.317562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 685565d0-b9dd-4a59-8e49-2d65e00ce9d2 · outbound

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

Explore the vulnerability of black-box models via diffusion models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 35

Resolution: malformed identifier; no resolver link, observed 2026-08-07T05:38:26.388096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 882318f0-82e7-487b-b17a-73d6027f28f5 · inbound