Paper Citation Record · LEDGER

Universal Adversarial Audio Perturbations

As of 14 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:1908.03173.

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

pith.paper-citation-record.v1
1908.03173 v5

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:26:39.775562Z

measured 70 of 70 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:08:37.186446Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:27:08.926506Z

Reference resolution

68 of 68 outbound references displayed

verified exact4
verified fuzzy55
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42affafc-3f1b-45de-be83-f6cae8e78cdf · outbound

This paper cites Object detection with deep learning: A review,.

Universal Adversarial Audio Perturbations Object detection with deep learning: A review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.694977Z

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 8e584608-52ed-4976-9d63-7b9e7e53146a · outbound

This paper cites Multilingual anchoring: Interactive topic modeling and alignment across languages,.

Universal Adversarial Audio Perturbations Multilingual anchoring: Interactive topic modeling and alignment across languages,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.685711Z

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 15e9fba5-9ed7-4113-acf4-ccf33899fd1d · outbound

This paper cites Unsupervised text style transfer using language models as dis- criminators,.

Universal Adversarial Audio Perturbations Unsupervised text style transfer using language models as dis- criminators,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.677088Z

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 bf4eec48-6a9c-42af-8983-344fed27184c · outbound

This paper cites Transfer learning from speaker veriﬁcation to multispeaker text-to-speech synthesis,.

Universal Adversarial Audio Perturbations Transfer learning from speaker veriﬁcation to multispeaker text-to-speech synthesis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.667333Z

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 cdac56bf-9ec1-4ec5-9a0e-476fd70f3b2e · outbound

This paper cites Unsupervised cross-modal alignment of speech and text embedding spaces,.

Universal Adversarial Audio Perturbations Unsupervised cross-modal alignment of speech and text embedding spaces,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.658645Z

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 45880274-897e-4bcd-972c-fd6c09e1f3a2 · outbound

This paper cites Intriguing properties of neural networks,.

Universal Adversarial Audio Perturbations Intriguing properties of neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.649513Z

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 cfe11c97-b3ac-4f6d-8445-d8c4701d46c2 · outbound

This paper cites Explaining and Har- nessing Adversarial Examples,.

Universal Adversarial Audio Perturbations Explaining and Har- nessing Adversarial Examples,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.640880Z

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 e8b23386-af0f-4649-92f8-bf6dc3058b48 · outbound

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

Universal Adversarial Audio Perturbations Towards evaluating the robustness of neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.632250Z

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 9b474184-c161-4142-9c62-4b6b724a172d · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: A survey,.

Universal Adversarial Audio Perturbations Threat of adversarial attacks on deep learning in computer vision: A survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.622759Z

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 49803a54-5df7-4bea-b4fc-2182b63360d6 · outbound

This paper cites On the security relevance of weights in deep learning.

Universal Adversarial Audio Perturbations On the security relevance of weights in deep learning

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T14:26:40.055334Z

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 97732fec-aaf8-4573-840b-797c3897c86e · outbound

This paper cites Cross-representation transferability of adversarial attacks: From spectrograms to audio waveforms,.

Universal Adversarial Audio Perturbations Cross-representation transferability of adversarial attacks: From spectrograms to audio waveforms,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.613864Z

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 5fdbc347-24b7-4f34-a7b9-ff9a2f43a704 · outbound

This paper cites Wild patterns: Ten years after the rise of adversarial machine learning,.

Universal Adversarial Audio Perturbations Wild patterns: Ten years after the rise of adversarial machine learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.605002Z

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 b6c8e03d-0282-43e1-8dcf-02932ddd7e81 · outbound

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

Universal Adversarial Audio Perturbations Knockoff nets: Stealing functionality of black-box models,

Reference 13

Resolution: verified exact; raw_fallback, observed 2026-08-14T14:26:40.040183Z

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 6d8fe745-a81a-4b4f-9242-e859d5f41cdb · outbound

This paper cites Universal adversarial perturbations,.

Universal Adversarial Audio Perturbations Universal adversarial perturbations,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.596091Z

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 bbb72529-0580-4065-a687-141580d91046 · outbound

This paper cites Deep Speech: Scaling up end-to-end speech recognition.

Universal Adversarial Audio Perturbations Deep Speech: Scaling up end-to-end speech recognition

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.610366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8efc910b-fadf-4885-97d4-965ec5e8f0bf · outbound

This paper cites Speech acoustic mod- eling from raw multichannel waveforms,.

Universal Adversarial Audio Perturbations Speech acoustic mod- eling from raw multichannel waveforms,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.586185Z

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 954ac846-7841-4bfa-a88a-73becbfc30dc · outbound

This paper cites Wavenet: A generative model for raw audio,.

Universal Adversarial Audio Perturbations Wavenet: A generative model for raw audio,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.577153Z

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 135b5eb2-13cf-4a20-8e49-5d7279078da5 · outbound

This paper cites Learning the speech front-end with raw waveform CLDNNs,.

Universal Adversarial Audio Perturbations Learning the speech front-end with raw waveform CLDNNs,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.568466Z

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 78a20c73-29f5-4c48-ac58-fd208382ab19 · outbound

This paper cites Speaker recognition from raw wave- form with SincNet,.

Universal Adversarial Audio Perturbations Speaker recognition from raw wave- form with SincNet,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.559730Z

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 105d7d10-5cee-4d40-aba1-4ba47dfa0375 · outbound

This paper cites Learning ﬁlterbanks from raw speech for phone recognition,.

Universal Adversarial Audio Perturbations Learning ﬁlterbanks from raw speech for phone recognition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.550349Z

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 44784f24-c6fd-47a2-956a-33d1963de28e · outbound

This paper cites End-to-end speech recognition from the raw wave- form,.

Universal Adversarial Audio Perturbations End-to-end speech recognition from the raw wave- form,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.540336Z

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 a2c8d722-3485-4784-8d5a-4d0a80b51d87 · outbound

This paper cites Audio adversarial examples: Targeted attacks on speech-to-text,.

Universal Adversarial Audio Perturbations Audio adversarial examples: Targeted attacks on speech-to-text,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.531937Z

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 323bcdd8-741a-4f46-bf8f-06325285da28 · outbound

This paper cites SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems.

Universal Adversarial Audio Perturbations SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:26:39.895355Z

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 b2ad5500-aa7e-48bb-9aef-10574e39cce6 · outbound

This paper cites Lower bounds on the robustness to adversarial perturbations,.

Universal Adversarial Audio Perturbations Lower bounds on the robustness to adversarial perturbations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.522781Z

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 8034af43-e44d-4029-a88b-51367a4c17f8 · outbound

This paper cites Are adversarial examples inevitable?.

Universal Adversarial Audio Perturbations Are adversarial examples inevitable?

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.513512Z

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 66f95460-66fc-427e-96ab-38a8f5534f0b · outbound

This paper cites Adversarial vulnerability for any classiﬁer,.

Universal Adversarial Audio Perturbations Adversarial vulnerability for any classiﬁer,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.503522Z

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 f4c2f481-c3af-4cdc-866a-509cdd98222a · outbound

This paper cites Adversarial examples in the physical world,.

Universal Adversarial Audio Perturbations Adversarial examples in the physical world,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.493713Z

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 15ad8e5f-1627-4904-8b5e-3dc031889165 · outbound

This paper cites Synthesizing Robust Adversarial Examples,.

Universal Adversarial Audio Perturbations Synthesizing Robust Adversarial Examples,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.484362Z

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 fcabd10b-f127-4c67-a556-08695b2c9f79 · outbound

This paper cites Hidden voice commands,.

Universal Adversarial Audio Perturbations Hidden voice commands,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.474925Z

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 d54ba917-c0c4-40d1-804b-9b9c1f1b073d · outbound

This paper cites Dolphi- nattack: Inaudible voice commands,.

Universal Adversarial Audio Perturbations Dolphi- nattack: Inaudible voice commands,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.466509Z

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 e3e1e333-1935-4ab1-a3b8-821d84bffa72 · outbound

This paper cites Crafting Adversarial Examples For Speech Paralinguistics Applications.

Universal Adversarial Audio Perturbations Crafting Adversarial Examples For Speech Paralinguistics Applications

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.660243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16f1c541-ca18-47c4-82ca-159f3f1616e2 · outbound

This paper cites Deep learning and music adversaries,.

Universal Adversarial Audio Perturbations Deep learning and music adversaries,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.457279Z

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 34614abb-c535-49ae-a226-868fc3d8307e · outbound

This paper cites Sirenattack: Generating adversarial audio for end-to-end acoustic systems,.

Universal Adversarial Audio Perturbations Sirenattack: Generating adversarial audio for end-to-end acoustic systems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.447170Z

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 1a05e8c4-5634-4152-a420-fb978dd3dbe7 · outbound

This paper cites Did you hear that? Adversarial Examples Against Automatic Speech Recognition.

Universal Adversarial Audio Perturbations Did you hear that? Adversarial Examples Against Automatic Speech Recognition

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.669574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a92d1c5-747d-4441-8aa9-5550ab69645f · outbound

This paper cites Convolutional neural networks for small-footprint keyword spotting,.

Universal Adversarial Audio Perturbations Convolutional neural networks for small-footprint keyword spotting,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.437966Z

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 ebf6f359-758b-400c-b2c2-417770de79ff · outbound

This paper cites Robust audio adversarial example for a physical attack,.

Universal Adversarial Audio Perturbations Robust audio adversarial example for a physical attack,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.427289Z

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 cb21d528-591c-4012-bc04-223f864c972f · outbound

This paper cites Imperceptible, robust, and targeted adversarial examples for automatic speech recognition,.

Universal Adversarial Audio Perturbations Imperceptible, robust, and targeted adversarial examples for automatic speech recognition,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.417063Z

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 ee63ff93-ac58-44a3-96d3-dae12be3537d · outbound

This paper cites Robustness of classiﬁers to universal perturbations: A geometric perspective,.

Universal Adversarial Audio Perturbations Robustness of classiﬁers to universal perturbations: A geometric perspective,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.407349Z

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 63b4db87-53cf-4ef2-b233-be8bea49fc7e · outbound

This paper cites Universal adversarial perturbations against semantic image segmentation,.

Universal Adversarial Audio Perturbations Universal adversarial perturbations against semantic image segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.397134Z

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 cb6b653c-83e1-4bbc-b5b9-2278a19a0a75 · outbound

This paper cites Universal adversarial attacks on text classiﬁers,.

Universal Adversarial Audio Perturbations Universal adversarial attacks on text classiﬁers,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.388015Z

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 dedc29ff-b631-4808-840d-58ffc40323c4 · outbound

This paper cites Learning universal adversarial pertur- bations with generative models,.

Universal Adversarial Audio Perturbations Learning universal adversarial pertur- bations with generative models,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.378409Z

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 89096cad-2639-480a-989e-e3c6b93c160a · outbound

This paper cites Universal adversarial perturbations for speech recognition systems,.

Universal Adversarial Audio Perturbations Universal adversarial perturbations for speech recognition systems,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.368140Z

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 c1df5d28-4a55-4c90-8a1b-ea7e58b6f6d9 · outbound

This paper cites Decoupling direction and norm for efﬁcient gradient- based L2 adversarial attacks and defenses,.

Universal Adversarial Audio Perturbations Decoupling direction and norm for efﬁcient gradient- based L2 adversarial attacks and defenses,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.357602Z

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 dead9185-d458-4954-9ef7-3cb648382ac1 · outbound

This paper cites Deepfool: a simple and accurate method to fool deep neural networks,.

Universal Adversarial Audio Perturbations Deepfool: a simple and accurate method to fool deep neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.248405Z

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 2975e393-6132-433a-ab80-c93c0d8c37ef · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Universal Adversarial Audio Perturbations Adam: A Method for Stochastic Optimization

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.703322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52481fa0-ae4e-48bc-a248-2a9683ee71bf · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization,.

Universal Adversarial Audio Perturbations Adaptive subgradient methods for online learning and stochastic optimization,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.238751Z

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 e64047fa-72b7-47b8-b4af-a7b92da9c8e3 · outbound

This paper cites On the impor- tance of initialization and momentum in deep learning,.

Universal Adversarial Audio Perturbations On the impor- tance of initialization and momentum in deep learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.229597Z

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 fa63ceb4-c385-40a3-9960-4fc7465ee548 · outbound

This paper cites Goodfellow, Y.

Universal Adversarial Audio Perturbations Goodfellow, Y

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.712173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b73967f-0522-4f8a-b448-0bfebbc10a58 · outbound

This paper cites A dataset and taxonomy for urban sound research,.

Universal Adversarial Audio Perturbations A dataset and taxonomy for urban sound research,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.214434Z

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 467f1c4f-0d6e-4e35-b38b-bce0909e3de8 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Universal Adversarial Audio Perturbations Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.718332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a216384-e919-4e98-ae52-8b31240ad1b6 · outbound

This paper cites Learning from between- class examples for deep sound recognition,.

Universal Adversarial Audio Perturbations Learning from between- class examples for deep sound recognition,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.204861Z

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 03e26e2c-ec90-4dc6-9c75-5060d1d5763e · outbound

This paper cites End-to-end envi- ronmental sound classiﬁcation using a 1D convolutional neural network,.

Universal Adversarial Audio Perturbations End-to-end envi- ronmental sound classiﬁcation using a 1D convolutional neural network,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.195746Z

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 86debf37-1c96-4110-bb48-e503c88102b5 · outbound

This paper cites Characterizing audio adversarial examples using temporal dependency,.

Universal Adversarial Audio Perturbations Characterizing audio adversarial examples using temporal dependency,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.186511Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fc7ea7e5-8e56-489f-b089-d9fb464312b2 · outbound

This paper cites Very deep convolutional net- works for large-scale image recognition,.

Universal Adversarial Audio Perturbations Very deep convolutional net- works for large-scale image recognition,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.177390Z

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 0938e1b0-10bb-42dd-9075-2d73e0fe8565 · outbound

This paper cites Adversarial Attacks in Sound Event Classification.

Universal Adversarial Audio Perturbations Adversarial Attacks in Sound Event Classification

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:26:39.838210Z

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 431ca266-96ae-4570-af87-7ccc4d852b12 · outbound

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

Universal Adversarial Audio Perturbations Delving into transferable adversarial examples and black-box attacks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.167487Z

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 dc53fe92-4009-4de4-897e-114b5d49520a · outbound

This paper cites Class-Conditional Defense GAN Against End-to-End Speech Attacks.

Universal Adversarial Audio Perturbations Class-Conditional Defense GAN Against End-to-End Speech Attacks

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:26:39.823446Z

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 f0fa0b65-eae5-4577-90c5-e6f6d5f2789e · outbound

This paper cites A multiversion programming inspired approach to detecting audio adversarial examples,.

Universal Adversarial Audio Perturbations A multiversion programming inspired approach to detecting audio adversarial examples,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.156290Z

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 fe1c0f19-c27e-4331-81c7-d38bf20feb93 · outbound

This paper cites A robust ap- proach for securing audio classiﬁcation against adversarial at- tacks,.

Universal Adversarial Audio Perturbations A robust ap- proach for securing audio classiﬁcation against adversarial at- tacks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.145528Z

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 65039262-af2d-4f9b-bb96-397157d453c9 · outbound

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

Universal Adversarial Audio Perturbations Towards deep learning models resistant to adversarial attacks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.135024Z

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 1d17373f-47ca-43f2-a4b8-61c9c5e48df7 · outbound

This paper cites Train- ing augmentation with adversarial examples for robust speech recognition,.

Universal Adversarial Audio Perturbations Train- ing augmentation with adversarial examples for robust speech recognition,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.124983Z

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 596c166c-16cd-4ecc-90b5-1938ecadfa63 · outbound

This paper cites Johnson, I.

Universal Adversarial Audio Perturbations Johnson, I

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.114648Z

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 3f70b13c-b62c-48db-b207-5b586823bb26 · outbound

This paper cites Adadelta: An adaptive learning rate method,.

Universal Adversarial Audio Perturbations Adadelta: An adaptive learning rate method,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.760493Z

Source-reported events for the cited work

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Observation 1a173858-2e5a-4353-a8bc-9e86aa7f63ae · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Universal Adversarial Audio Perturbations Dropout: a simple way to prevent neural networks from overﬁtting

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.098221Z

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 bb176dd3-7dfb-44eb-8a05-08a6d552f723 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Universal Adversarial Audio Perturbations Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.087590Z

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 08d8fd87-e8b3-4b85-b92f-abc2c93d2079 · outbound

This paper cites Layer Normalization.

Universal Adversarial Audio Perturbations Layer Normalization

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:39.769376Z

Source-reported events for the cited work

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Observation 38382769-0c61-4f3c-b853-6bce2e2a7c90 · outbound

This paper cites Rectiﬁer nonlinearities improve neural network acoustic models,.

Universal Adversarial Audio Perturbations Rectiﬁer nonlinearities improve neural network acoustic models,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.076877Z

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 063f0196-69e1-456b-9f15-838cd18e38b4 · outbound

This paper cites His research interests include audio and speech processing, music information retrieval and developing ad- versarial attacks on machine learning systems.

Universal Adversarial Audio Perturbations His research interests include audio and speech processing, music information retrieval and developing ad- versarial attacks on machine learning systems

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:40.066019Z

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.

Pith citing papers

Observation 5e952512-06f4-49e2-9de6-ac5ce587f6f9 · inbound

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models cites this paper.

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models Universal Adversarial Audio Perturbations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:08:37.186446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce8cfe29-59a7-463f-963b-2f1a5a251d02 · inbound

Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks cites this paper.

Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks Universal Adversarial Audio Perturbations

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:27:08.927938Z

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.