Paper Citation Record · LEDGER

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2509.04980.

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

pith.paper-citation-record.v1
2509.04980 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:48:59.145297Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:48:58.918842Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T05:48:59.217967Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4d3c01f-f688-4541-99ba-94e61c18135a · outbound

This paper cites MAIA: An Inpainting-Based Approach for Music Adversarial Attacks.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks MAIA: An Inpainting-Based Approach for Music Adversarial Attacks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.920365Z

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Observation bb268054-db72-4225-989a-413beed55e3c · outbound

This paper cites an unresolved cited work.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 2

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Observation f6d14873-48c8-4998-b042-cc87f22aab8e · outbound

This paper cites This framework reconstructs critical audio segments with adversarial perturbations, ensuring musical coher- ence while effectively misleading target models.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks This framework reconstructs critical audio segments with adversarial perturbations, ensuring musical coher- ence while effectively misleading target models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.905108Z

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Observation ae7bc4ce-596e-4489-98f4-153c7f67012a · outbound

This paper cites an unresolved cited work.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 4

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Observation 3e245a71-ca8a-471c-a962-d91bde25ee47 · outbound

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MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 5

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Observation 262652a9-1043-4224-9c5c-e6d69f8739a5 · outbound

This paper cites In practical terms, modi- fying only the most influential time-frequency regions can reduce the extent of injected noise, thereby decreasing per- ceptual artifacts.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks In practical terms, modi- fying only the most influential time-frequency regions can reduce the extent of injected noise, thereby decreasing per- ceptual artifacts

Reference 6

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Observation efd7024a-3a1b-430a-bcf5-accdb89223f8 · outbound

This paper cites an unresolved cited work.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 7

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Observation 5903fc00-6005-4ebf-aa0c-99823f37657c · outbound

This paper cites We then process each segment sequentially, prioritizing those with the highest impact.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks We then process each segment sequentially, prioritizing those with the highest impact

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.822454Z

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Observation 86d5c05a-8fb4-4559-a515-ec871aafc1d5 · outbound

This paper cites an unresolved cited work.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 9

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Observation 7a25d7f0-053b-451e-8abf-10ddb8bc0bae · outbound

This paper cites Query-efficient adversarial attack with low perturbation against end-to-end speech recognition systems,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Query-efficient adversarial attack with low perturbation against end-to-end speech recognition systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.597905Z

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Observation 53aafed9-7386-4e5b-a66e-82bb7a9a5b4c · outbound

This paper cites an unresolved cited work.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Unresolved cited work

Reference 11

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Observation 5313faed-a9e5-4eaf-8644-da0af707200c · outbound

This paper cites BG2024027), the Suzhou Science and Technol- ogy Development Planning Programme (Gusu Innovation and Entrepreneurship Leading Talents Program, Grant No.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks BG2024027), the Suzhou Science and Technol- ogy Development Planning Programme (Gusu Innovation and Entrepreneurship Leading Talents Program, Grant No

Reference 12

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Observation ffc8b35b-6596-4faa-8830-ca3bde0ec50b · outbound

This paper cites Low-resource music genre classification with cross- modal neural model reprogramming,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Low-resource music genre classification with cross- modal neural model reprogramming,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.743181Z

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Observation 30fb0e44-cf33-49e8-b86e-ae38a05ec6a2 · outbound

This paper cites Music instrument recogni- tion using deep convolutional neural networks,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Music instrument recogni- tion using deep convolutional neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.727699Z

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Observation ef64db6f-f6ef-4c9d-b590-2a721f57b90e · outbound

This paper cites MAIA: An Inpainting-Based Approach for Music Adversarial Attacks.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks MAIA: An Inpainting-Based Approach for Music Adversarial Attacks

Reference 15

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Observation 05a026d4-563a-42c2-b91c-8d4f71756617 · outbound

This paper cites Byte- cover: Cover song identification via multi-loss train- ing,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Byte- cover: Cover song identification via multi-loss train- ing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.711672Z

Source-reported events for the cited work

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Observation f7b7a339-c19c-42af-b961-bf73a5bb9cd3 · outbound

This paper cites Byte- cover2: Towards dimensionality reduction of latent embedding for efficient cover song identification,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Byte- cover2: Towards dimensionality reduction of latent embedding for efficient cover song identification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.696371Z

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Observation ecfbeb66-62b6-4bdc-82d3-635d1816f6bc · outbound

This paper cites Bytecover3: Accurate cover song identifica- tion on short queries,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Bytecover3: Accurate cover song identifica- tion on short queries,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.680787Z

Source-reported events for the cited work

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Observation 68506284-afa1-410e-b6c2-b11c398307e8 · outbound

This paper cites An emotional recommender system for music,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks An emotional recommender system for music,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.664890Z

Source-reported events for the cited work

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Observation 6b91fc84-9cad-47f4-bc65-fcac99228d24 · outbound

This paper cites Explainability in mu- sic recommender systems,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Explainability in mu- sic recommender systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.647651Z

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Observation 55335a43-9cef-49e2-86ed-c9592f12520d · outbound

This paper cites On end-to-end white-box adversarial attacks in music information re- trieval.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks On end-to-end white-box adversarial attacks in music information re- trieval

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.630405Z

Source-reported events for the cited work

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Observation 247fb04d-7c4f-4a2e-a844-132a9132e298 · outbound

This paper cites Adver- sarial attacks on copyright detection systems,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Adver- sarial attacks on copyright detection systems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.614062Z

Source-reported events for the cited work

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Observation 606b5b84-6831-4fb1-9734-e82f4750bc41 · outbound

This paper cites Devil’s whisper: A general approach for physical adversarial attacks against com- mercial black-box speech recognition devices,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Devil’s whisper: A general approach for physical adversarial attacks against com- mercial black-box speech recognition devices,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.581665Z

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Observation 748a0aef-4219-4965-a078-9b34b4223360 · outbound

This paper cites Towards evaluating the ro- bustness of neural networks,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Towards evaluating the ro- bustness of neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.565689Z

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Observation 73d8b2fa-fe32-4d75-bd48-dc29596cc03f · outbound

This paper cites Mind the box:l_1-apgd for sparse adversarial attacks on image classifiers,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Mind the box:l_1-apgd for sparse adversarial attacks on image classifiers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.550348Z

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Observation 23a255ce-5193-4106-8b07-bb9ff1f0d2ef · outbound

This paper cites Deep learning and music adversaries,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Deep learning and music adversaries,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.534573Z

Source-reported events for the cited work

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Observation d1cb7770-0f52-4827-a4d8-8bad268fddc4 · outbound

This paper cites Perception-aware attack: Creating adversarial music via reverse-engineering human perception,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Perception-aware attack: Creating adversarial music via reverse-engineering human perception,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.518380Z

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Observation f5fe536e-3909-48e2-8aaa-6953d69b7e78 · outbound

This paper cites SMACK: Decoupling source language details from verifier implementa- tions,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks SMACK: Decoupling source language details from verifier implementa- tions,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.501146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a44c2ba-0cba-490e-9f51-c47e76027c42 · outbound

This paper cites Frequency-driven imperceptible adversarial attack on semantic similarity,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Frequency-driven imperceptible adversarial attack on semantic similarity,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.484281Z

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Observation 4ab5e5b1-7597-4a62-8e2b-eb700397fe67 · outbound

This paper cites Ex- plainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelli- gence,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Ex- plainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelli- gence,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.468616Z

Source-reported events for the cited work

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Observation d71a319a-6763-4b73-802b-74628e1c731c · outbound

This paper cites Learning deep features for discriminative localization,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Learning deep features for discriminative localization,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.452531Z

Source-reported events for the cited work

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Observation d0b7db1a-4e09-43f7-b483-3f83f746a7e9 · outbound

This paper cites Network In Network.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Network In Network

Reference 32

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This paper cites Grad-cam: Visual explana- tions from deep networks via gradient-based localiza- tion,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Grad-cam: Visual explana- tions from deep networks via gradient-based localiza- tion,

Reference 33

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Observation 2e6dad3a-908d-4021-80fc-36877871df58 · outbound

This paper cites Is ob- ject localization for free?-weakly-supervised learning with convolutional neural networks,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Is ob- ject localization for free?-weakly-supervised learning with convolutional neural networks,

Reference 34

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MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Pytorch library for cam methods,

Reference 35

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Observation e48e400a-1051-416f-80c1-415cd71bb1f5 · outbound

This paper cites GACELA: A generative adversarial con- text encoder for long audio inpainting of music,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks GACELA: A generative adversarial con- text encoder for long audio inpainting of music,

Reference 36

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Observation d9548c93-fc0d-4278-9290-c9792d550411 · outbound

This paper cites A comparative study of large-scale variants of cma-es,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks A comparative study of large-scale variants of cma-es,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.370500Z

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Observation a5754ca6-1292-42b9-9049-b66afe76cea7 · outbound

This paper cites Coverhunter: Cover song identification with refined attention and alignments,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Coverhunter: Cover song identification with refined attention and alignments,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.354801Z

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Observation 0fe497c3-c87c-45a7-8d55-07f8256808db · outbound

This paper cites Key-invariant convolu- tional neural network toward efficient cover song iden- tification,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Key-invariant convolu- tional neural network toward efficient cover song iden- tification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.339608Z

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Observation d69d88a6-d1f6-47bc-93dd-7cc888e15c05 · outbound

This paper cites The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

Reference 40

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Source-reported events for the cited work

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Observation 2f499e26-f23b-425a-b8ac-88b6849d20f4 · outbound

This paper cites MERT: Acoustic music understanding model with large-scale self-supervised training,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks MERT: Acoustic music understanding model with large-scale self-supervised training,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.323833Z

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Observation 32f6d93c-ef05-4ba7-ac12-2cac42e24149 · outbound

This paper cites Fréchet Audio Distance: A reference-free metric for evaluating music enhancement algorithms,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Fréchet Audio Distance: A reference-free metric for evaluating music enhancement algorithms,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.306357Z

Source-reported events for the cited work

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Observation bcd3c545-6633-4cad-a452-f8b194e320de · outbound

This paper cites Distance measures for speech processing,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Distance measures for speech processing,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.289693Z

Source-reported events for the cited work

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Observation 7ea82943-6570-42bd-8216-50267c95b4ee · outbound

This paper cites Universal adversarial at- tack via enhanced projected gradient descent,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Universal adversarial at- tack via enhanced projected gradient descent,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.273308Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2629d16-89cd-4075-b978-83008aab2554 · outbound

This paper cites Natural evolution strate- gies,.

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Natural evolution strate- gies,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.256672Z

Source-reported events for the cited work

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Observation 9acb94a7-0754-40fa-8847-0f1615359380 · outbound

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

MAIA: An Inpainting-Based Approach for Music Adversarial Attacks Zoo: Zeroth order optimization based black- box attacks to deep neural networks without training substitute models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:48:59.241162Z

Source-reported events for the cited work

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Pith citing papers

Observation ef64db6f-f6ef-4c9d-b590-2a721f57b90e · inbound