Paper Citation Record · LEDGER

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:1908.07000.

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

pith.paper-citation-record.v1
1908.07000 v3

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:36:53.007232Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

45 of 45 outbound references displayed

verified exact1
verified fuzzy33
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c927202f-c157-4e33-a552-378e96263446 · outbound

This paper cites There are No Bit Parts for Sign Bits in Black-Box Attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries There are No Bit Parts for Sign Bits in Black-Box Attacks

Reference 1

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Source-reported events for the cited work

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Observation d4ea7c74-64a2-42b3-a2f2-1001a9f31e6a · outbound

This paper cites GenAttack: Practical black-box attacks with gradient-free optimization.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries GenAttack: Practical black-box attacks with gradient-free optimization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.546145Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e109382f-3116-4860-84c7-31f9588a92c2 · outbound

This paper cites The shat- tered gradients problem: If resnets are the answer, then what is the question? In International Conference on Machine Learning, 2017.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries The shat- tered gradients problem: If resnets are the answer, then what is the question? In International Conference on Machine Learning, 2017

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.535285Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3bd64939-78aa-45e9-999c-8f006b177c8a · outbound

This paper cites Exploring the space of black-box attacks on deep neural networks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Exploring the space of black-box attacks on deep neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.523996Z

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Observation dc7fdef3-f343-406d-8e94-af075ac0fda6 · outbound

This paper cites Decision-based adversarial attacks: Reliable attacks against black-box machine learning models.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Decision-based adversarial attacks: Reliable attacks against black-box machine learning models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.512298Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f04b93b6-81e1-448e-b8d5-5aedf3beaafa · outbound

This paper cites Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.853734Z

Source-reported events for the cited work

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Observation fc6fd853-eb9d-44e5-9f2e-5b0677674755 · outbound

This paper cites Prototypical examples in deep learning: Metrics, characteristics, and utility.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Prototypical examples in deep learning: Metrics, characteristics, and utility

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.500086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d2fd696c-816f-4239-a2b7-0f11c6337a96 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Towards evaluating the robustness of neural networks

Reference 8

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Source-reported events for the cited work

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Observation 9ed18ac7-1138-4ac8-973b-97ddc3540e03 · outbound

This paper cites HopSkipJumpAttack: A Query-Efficient Decision-Based Attack.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.865399Z

Source-reported events for the cited work

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Observation d6fd34bd-dc6e-4741-898e-7e2d4393e16b · outbound

This paper cites ZOO: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries ZOO: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.481637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01cb798a-bf6b-49b7-972f-d280b6dc6689 · outbound

This paper cites Stateful Detection of Black-Box Adversarial Attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Stateful Detection of Black-Box Adversarial Attacks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.872996Z

Source-reported events for the cited work

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Observation 1ad0d5e2-2ac9-4c73-be6b-b247ea88428e · outbound

This paper cites Query-eﬃcient hard- label black-box attack: An optimization-based approach.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Query-eﬃcient hard- label black-box attack: An optimization-based approach

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.469840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 32d6c308-f560-4999-9f62-bd3d29239e50 · outbound

This paper cites Improving Black-box Adversarial Attacks with a Transfer-based Prior.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Improving Black-box Adversarial Attacks with a Transfer-based Prior

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:36:53.068868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4fe6dc5f-e6e0-49fe-baba-351fddeae7b6 · outbound

This paper cites an unresolved cited work.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:36:53.457165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 42f09885-e64e-4ea6-a775-b8bf0ffb0e06 · outbound

This paper cites Boosting adver- sarial attacks with momentum.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Boosting adver- sarial attacks with momentum

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.445876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a97d8c20-bbf2-4c69-b000-496a2e92426e · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.434611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 086487d9-3d2a-4a45-963d-eadb0b61805f · outbound

This paper cites Explaining and harnessing adversarial exam- ples.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Explaining and harnessing adversarial exam- ples

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.423102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f287cbf4-871d-49a5-813d-a4ea45c96756 · outbound

This paper cites Simple black-box adversarial attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Simple black-box adversarial attacks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.411820Z

Source-reported events for the cited work

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Observation 326f844e-c81f-4ff3-92ec-738384920d05 · outbound

This paper cites Deep residual learning for image recognition.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Deep residual learning for image recognition

Reference 19

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Source-reported events for the cited work

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Observation dcf56910-4f0e-4834-9c7d-06c41d5c8eb7 · outbound

This paper cites Densely connected convolutional networks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Densely connected convolutional networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.393899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b5d2469-7566-42d2-845f-7135dd2f739b · outbound

This paper cites Black-box adversarial attacks with limited queries and information.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Black-box adversarial attacks with limited queries and information

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.382700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7aed996f-1ff6-410e-9590-b402a7a14336 · outbound

This paper cites Prior convictions: Black-box adversarial attacks with bandits and priors.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Prior convictions: Black-box adversarial attacks with bandits and priors

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.370600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cdea9880-7efb-42b7-8cb4-ceeeb74cc6de · outbound

This paper cites Learning mul- tiple layers of features from tiny images.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Learning mul- tiple layers of features from tiny images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.359414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3e160da5-1a21-4812-abd6-20d324a1c4c9 · outbound

This paper cites Ad- versarial examples in the physical world.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Ad- versarial examples in the physical world

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.348236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7305ea39-7613-4f7c-af0e-07a0a6a7cf97 · outbound

This paper cites The MNIST database of handwritten digits.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries The MNIST database of handwritten digits

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.336719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3479dbf5-0696-4d6f-922f-ebe8fd56af6b · outbound

This paper cites Gradient-based learning applied to document recognition.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Gradient-based learning applied to document recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.324947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f224a487-187f-4656-ad18-ec9655c3b44f · outbound

This paper cites Query- eﬃcient black-box attack by active learning.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Query- eﬃcient black-box attack by active learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.312421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c1111c6-8f45-4b43-b49c-448d630ec465 · outbound

This paper cites Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.300996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fe8a15ff-c5e3-4887-80b8-4da9fa8df770 · outbound

This paper cites Delving into transferable adversarial examples and black-box attacks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Delving into transferable adversarial examples and black-box attacks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.289136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2bff7b8f-6b51-4580-b918-07f75d3b2b4d · outbound

This paper cites CIFAR10 adversarial examples challenge.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries CIFAR10 adversarial examples challenge

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.277405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 80e30964-fd15-4b45-9a05-49ae222ba92f · outbound

This paper cites MNIST adversarial examples chal- lenge.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries MNIST adversarial examples chal- lenge

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.265233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3b4f8312-fdc3-4c1e-a45e-f2e1a3ec97e9 · outbound

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

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Towards deep learning models resistant to adversarial attacks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.954763Z

Source-reported events for the cited work

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Observation bf956622-d3e8-4db0-bcae-2cf776dc3e18 · outbound

This paper cites Parsi- monious black-box adversarial attacks via eﬃcient com- binatorial optimization.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Parsi- monious black-box adversarial attacks via eﬃcient com- binatorial optimization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.245748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 690e5be7-7f52-498d-badf-c58f651d0a84 · outbound

This paper cites Simple black-box adversarial perturbations for deep net- works.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Simple black-box adversarial perturbations for deep net- works

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.232124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 53a9e5ad-6222-46f5-9c8d-a341b7538e1f · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.967237Z

Source-reported events for the cited work

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Observation c1d870e2-5fbd-4246-8cc7-d5a5d369bbe2 · outbound

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

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Practical black-box attacks against machine learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.220861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 374fe4ae-b579-445d-8ace-4db6db4cb515 · outbound

This paper cites Very deep con- volutional networks for large-scale image recognition.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Very deep con- volutional networks for large-scale image recognition

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.208295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a85e7d22-a2ac-45f3-961d-1ed8ba264a5a · outbound

This paper cites Query-limited black-box attacks to classiﬁers.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Query-limited black-box attacks to classiﬁers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.196961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b9324eae-71e1-4226-bb58-bb724fec0d5a · outbound

This paper cites Intriguing properties of neural networks.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Intriguing properties of neural networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.982325Z

Source-reported events for the cited work

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Observation 82248e7a-0a14-4674-9a55-67d9c81a6dee · outbound

This paper cites Targeted Adversarial Examples for Black Box Audio Systems.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Targeted Adversarial Examples for Black Box Audio Systems

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-14T12:36:52.985909Z

Source-reported events for the cited work

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Observation 4c645395-f339-4df5-baa7-9bfd8498b173 · outbound

This paper cites En- semble adversarial training: Attacks and defenses.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries En- semble adversarial training: Attacks and defenses

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.176011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c2f692a0-a2b1-4605-9e63-5a29e648a3fb · outbound

This paper cites Robustness may be at odds with accuracy.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Robustness may be at odds with accuracy

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.163858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 048afd41-9abb-4e3a-99f2-4c2efccdd58d · outbound

This paper cites Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural net- works.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural net- works

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.149693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f9905f0-f2ac-4035-8876-610bed5b0a2f · outbound

This paper cites Natural evolution strategies.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Natural evolution strategies

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.137103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a441e8b5-9d45-4e8a-85bc-eee9c9f9a129 · outbound

This paper cites Improving transferability of adversarial examples with input diversity.

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries Improving transferability of adversarial examples with input diversity

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:36:53.124658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.