Paper Citation Record · LEDGER

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach

As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.15739.

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

pith.paper-citation-record.v1
2502.15739 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:57:09.197549Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:09:37.781890Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:09:41.232647Z

Reference resolution

51 of 51 outbound references displayed

verified exact4
verified fuzzy28
unresolved19
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be61b48c-1a4c-415b-8ef0-f879c1fd07d2 · outbound

This paper cites Number of smartphone users worldwide (bil- lions),.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Number of smartphone users worldwide (bil- lions),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.162791Z

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Observation c661a9d8-b69e-4ce8-91bf-59d06433fe4c · outbound

This paper cites Identifying and analyzing the privacy of apps for kids,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Identifying and analyzing the privacy of apps for kids,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.149515Z

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Observation 2674ddf7-114d-48d1-ac64-7f33e15ca988 · outbound

This paper cites The common sense census: Media use by tweens and teens,,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach The common sense census: Media use by tweens and teens,,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.137007Z

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Observation f7326fde-2df2-4b07-b79d-bd9a4cc6ab18 · outbound

This paper cites Build teacher approved apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Build teacher approved apps,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.124225Z

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Observation 84b9cab8-bd4c-4dbd-98ea-8c76dde3c4cb · outbound

This paper cites Automatic content inspection and forensics for children Android apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Automatic content inspection and forensics for children Android apps,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.112249Z

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Observation 4aa82813-9a3c-4260-a9a9-287374ccacca · outbound

This paper cites Qustodio releases 2023 annual report, born connected: The rise of the ai generation,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Qustodio releases 2023 annual report, born connected: The rise of the ai generation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.100146Z

Source-reported events for the cited work

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Observation 7d0986ed-c76a-4ff0-81f5-8e1f73c9b610 · outbound

This paper cites Not seen, not heard in the digital world! measuring privacy practices in children’s apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Not seen, not heard in the digital world! measuring privacy practices in children’s apps,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.087390Z

Source-reported events for the cited work

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Observation 60bebc58-f4e2-459e-9703-42f7e659b9e2 · outbound

This paper cites Developer content policy,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Developer content policy,

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-08T21:57:09.694772Z

Source-reported events for the cited work

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Observation 0e785983-a19d-4009-aedd-82b2c1911cad · outbound

This paper cites Android’s latest statistics 2024: How many people have Androids?.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Android’s latest statistics 2024: How many people have Androids?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.074929Z

Source-reported events for the cited work

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Observation 791ea5a8-2da9-460d-9b17-e15f47ebfbc8 · outbound

This paper cites Developer content policy,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Developer content policy,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.062514Z

Source-reported events for the cited work

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Observation 4ada29f3-3198-4c78-9344-90769cca9439 · outbound

This paper cites Mobile apps for kids: Current privacy disclosures are disappointing,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Mobile apps for kids: Current privacy disclosures are disappointing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.049567Z

Source-reported events for the cited work

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Observation 74303a99-383d-433b-9436-18d774eaeeff · outbound

This paper cites Freely given consent? studying consent notice of third-party tracking and its violations of gdpr in Android apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Freely given consent? studying consent notice of third-party tracking and its violations of gdpr in Android apps,

Reference 12

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Observation 532cf98f-4017-4f92-9858-653b8059b026 · outbound

This paper cites Measuring user perception for detecting unexpected access to sensitive resource in mobile apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Measuring user perception for detecting unexpected access to sensitive resource in mobile apps,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.022429Z

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Observation afc809ac-187d-4078-8ee7-399f21901be3 · outbound

This paper cites Protecting your children from inappropriate content in mobile apps: An automatic ma- turity rating framework,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Protecting your children from inappropriate content in mobile apps: An automatic ma- turity rating framework,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:10.009417Z

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Observation c62fd509-f7b8-46ec-ac21-1bebc57e86d3 · outbound

This paper cites Can apps play by the coppa rules?.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Can apps play by the coppa rules?

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.996010Z

Source-reported events for the cited work

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Observation 45ba0d86-21d0-4f38-817b-03be0465c5e3 · outbound

This paper cites Is this app safe for children? a comparison study of maturity ratings on Android and iOS applications,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Is this app safe for children? a comparison study of maturity ratings on Android and iOS applications,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.982959Z

Source-reported events for the cited work

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Observation a17c38ed-8445-404d-8133-25f9e4d34ced · outbound

This paper cites Automatic maturity rating for Android apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Automatic maturity rating for Android apps,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.969653Z

Source-reported events for the cited work

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Observation d53f8190-b0b8-4faa-b527-52b71abc7e60 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach A simple framework for contrastive learning of visual representations,

Reference 18

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This paper cites Barlow twins: Self-supervised learning via redundancy reduction,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Barlow twins: Self-supervised learning via redundancy reduction,

Reference 19

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Observation 9311a117-d8b4-4048-9188-34576e174b72 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Learning transferable visual models from natural language supervision,

Reference 20

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This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 21

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Observation c09244ad-6a20-4005-b336-b5f9481e517b · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Representation Learning with Contrastive Predictive Coding

Reference 22

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This paper cites Lit: Zero-shot transfer with locked-image text tuning,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Lit: Zero-shot transfer with locked-image text tuning,

Reference 23

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This paper cites CLIP2Video: Mastering Video-Text Retrieval via Image CLIP.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

Reference 24

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach A straightforward framework for video retrieval using clip,

Reference 25

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Observation 4fae1c83-a41d-4cc4-9277-e800bb531892 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 26

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Explaining CLIP's performance disparities on data from blind/low vision users

Reference 27

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications

Reference 28

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Stable bias: Evaluating societal representations in diffusion models,

Reference 29

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Observation 205fa742-16ef-4963-a549-0fea986cf1db · outbound

This paper cites Masked siamese networks for label-efficient learning,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Masked siamese networks for label-efficient learning,

Reference 30

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This paper cites Masked au- toencoders are scalable vision learners,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Masked au- toencoders are scalable vision learners,

Reference 31

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Microsoft coco: Common objects in context,

Reference 32

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Observation 040412a5-73da-4e8b-8ca1-529054bee3a4 · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.858225Z

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Observation e29b8176-394d-4196-9714-858e15460ede · outbound

This paper cites Attention is all you need,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Attention is all you need,

Reference 34

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Sigmoid loss for language image pre-training,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.837071Z

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This paper cites Unified contrastive learning in image-text-label space,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Unified contrastive learning in image-text-label space,

Reference 36

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Observation a551d8ee-62c1-4973-b2d3-b4d7add0da13 · outbound

This paper cites Early detection of spam mobile apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Early detection of spam mobile apps,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.809137Z

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Observation 8f13554d-d7dd-430b-80b0-b7a7947a7cb8 · outbound

This paper cites A multi-modal neural embeddings approach for detecting mobile counterfeit apps,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach A multi-modal neural embeddings approach for detecting mobile counterfeit apps,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.796146Z

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Adam: A Method for Stochastic Optimization

Reference 39

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 40

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Deep residual learning for image recognition,

Reference 41

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 42

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 43

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach A multiscale visualization of attention in the transformer model,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.766126Z

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Observation 069c4533-63e0-421b-9e94-a23983c2c110 · outbound

This paper cites Manage target audience and app content settings.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Manage target audience and app content settings

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-08T21:57:09.441285Z

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Observation ba5cd3f1-3193-41bf-a2a8-4bbc6076f1e7 · outbound

This paper cites Detecting and Characterising Mobile App Metamorphosis in Google Play Store.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Detecting and Characterising Mobile App Metamorphosis in Google Play Store

Reference 46

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Observation 2783ec70-da1e-48a6-bd75-6c4133484536 · outbound

This paper cites How we fought bad apps and bad actors in 2023,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach How we fought bad apps and bad actors in 2023,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.752965Z

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Observation ce08d975-b65a-40fa-bb91-8881e503d5d7 · outbound

This paper cites A multi-modal neural embeddings approach for detecting mobile counterfeit apps: A case study on Google Play store,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach A multi-modal neural embeddings approach for detecting mobile counterfeit apps: A case study on Google Play store,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.739796Z

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Observation 87ceff84-91df-4fb8-b7c4-bd55c8b70f0b · outbound

This paper cites Comparing apples to Androids: Discovery, retrieval, and matching of and Android apps for cross-platform analyses,.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Comparing apples to Androids: Discovery, retrieval, and matching of and Android apps for cross-platform analyses,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T21:57:09.726777Z

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Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Unresolved cited work

Reference 2012

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This paper cites Before moving into research, he worked nearly six years in the telecommunications industry in core network plan- ning and operations.

Detecting Content Rating Violations in Android Applications: A Vision-Language Approach Before moving into research, he worked nearly six years in the telecommunications industry in core network plan- ning and operations

Reference 2015

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Pith citing papers

Observation b13c032d-f35f-4146-8673-0d482d3e0b85 · inbound

QwenSafe: Multimodal Content Rating Description Identification via Preference-Aligned VLMs cites this paper.

QwenSafe: Multimodal Content Rating Description Identification via Preference-Aligned VLMs Detecting Content Rating Violations in Android Applications: A Vision-Language Approach

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:09:41.234325Z

Source-reported events for the cited work

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