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Paper Citation Record · LEDGER

Generalizable Targeted Data Poisoning against Varying Physical Objects

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.03908.

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

pith.paper-citation-record.v1
2412.03908 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:05:21.658856Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy34
  • unresolved9
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c519394a-3418-4a31-a4db-0b1996cc0a0d · outbound

This paper cites Bullseye polytope: A scalable clean-label poisoning attack with improved trans- ferability.

Generalizable Targeted Data Poisoning against Varying Physical Objects Bullseye polytope: A scalable clean-label poisoning attack with improved trans- ferability

Reference 1

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verified fuzzy
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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.

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Observation 49287561-92a6-4a8c-a477-9ae4f4245a66 · outbound

This paper cites Poison- ing attacks against support vector machines.

Generalizable Targeted Data Poisoning against Varying Physical Objects Poison- ing attacks against support vector machines

Reference 2

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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.

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Observation b4c439b5-ef58-48e6-94b9-b125c3584217 · outbound

This paper cites Poisoning web-scale training datasets is practical.

Generalizable Targeted Data Poisoning against Varying Physical Objects Poisoning web-scale training datasets is practical

Reference 3

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Source-reported events for the cited work

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

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Observation e8e427ff-a337-4a0c-8171-a80ad7676c25 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts.

Generalizable Targeted Data Poisoning against Varying Physical Objects Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts

Reference 4

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unresolved
no resolver link, observed 2026-08-11T22:05:21.469298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.469298Z digest=sha256:8caf5acff182e8bbee9a9ee87af5ecb230a903c844f465a3fb01caac027c4d55

Observation 31452b6b-ad77-48f3-8f19-00d8f01266ea · outbound

This paper cites Wild patterns reloaded: A survey of machine learning security against training data poisoning.

Generalizable Targeted Data Poisoning against Varying Physical Objects Wild patterns reloaded: A survey of machine learning security against training data poisoning

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-11T22:05:22.259912Z

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.

source=pdf_text observed=2026-08-11T22:05:21.473878Z digest=sha256:7b936cbcee3ef5d0ad1b638e298e53316ae0a7eb13672558251e4899e105d9f4

Observation bb94a5d1-1796-4a0a-88bf-ec6404784a58 · outbound

This paper cites Robust unlearnable examples: Protecting data privacy against adversarial learning.

Generalizable Targeted Data Poisoning against Varying Physical Objects Robust unlearnable examples: Protecting data privacy against adversarial learning

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:22.244714Z

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.

source=pdf_text observed=2026-08-11T22:05:21.478800Z digest=sha256:00aef41ce2f5999adb3b1d920c2d1c8edb9e558da49d4a7e4336b750c2f21e9e

Observation 35bf5daa-312b-43c9-851a-a2cb2fd34fcb · outbound

This paper cites Dat- acomp: In search of the next generation of multimodal datasets.

Generalizable Targeted Data Poisoning against Varying Physical Objects Dat- acomp: In search of the next generation of multimodal datasets

Reference 7

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.229926Z

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.

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Observation a59f8fa6-527e-4e68-9a0a-c61772bf2f8a · outbound

This paper cites Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein.

Generalizable Targeted Data Poisoning against Varying Physical Objects Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein

Reference 8

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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.

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Observation e717f727-9ba5-43bc-9c29-79c005d9693f · outbound

This paper cites Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses.

Generalizable Targeted Data Poisoning against Varying Physical Objects Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses

Reference 9

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.200061Z

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.

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Observation 49af4650-4b74-405e-89d8-e6765e267d41 · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference.

Generalizable Targeted Data Poisoning against Varying Physical Objects Levit: a vision transformer in convnet’s clothing for faster inference

Reference 10

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no resolver link, observed 2026-08-11T22:05:21.498973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b8341511-dc48-410c-aa03-146cb2aa17c2 · outbound

This paper cites Badnets: Evaluating backdooring attacks on deep neu- ral networks.

Generalizable Targeted Data Poisoning against Varying Physical Objects Badnets: Evaluating backdooring attacks on deep neu- ral networks

Reference 11

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raw_fallback, observed 2026-08-11T22:05:22.175380Z

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.

source=pdf_text observed=2026-08-11T22:05:21.503813Z digest=sha256:f791035226859396360f759df704661411d7b82a7d793756003deddb3fce7b3e

Observation 21ae2857-357f-4fe7-832e-00efd6e05a87 · outbound

This paper cites On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping.

Generalizable Targeted Data Poisoning against Varying Physical Objects On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

Reference 12

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no resolver link, observed 2026-08-11T22:05:21.508776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9ddba6bb-ddba-4181-95cd-02f4812196ba · outbound

This paper cites Nat- uralistic physical adversarial patch for object detectors.

Generalizable Targeted Data Poisoning against Varying Physical Objects Nat- uralistic physical adversarial patch for object detectors

Reference 13

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.160491Z

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.

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Observation 547fb8e1-ee2f-4c22-ad35-71ac8f580434 · outbound

This paper cites Adversarial texture for fooling person detectors in the physical world.

Generalizable Targeted Data Poisoning against Varying Physical Objects Adversarial texture for fooling person detectors in the physical world

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T22:05:21.518338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.518338Z digest=sha256:01aab9920d7b3eca90fce3b9e78aacbd19faa771c1c823f6187e7970c630b9c9

Observation f8e36101-abc8-4e51-a989-4a670f62ff1c · outbound

This paper cites Unlearnable examples: Making personal data unexploitable.

Generalizable Targeted Data Poisoning against Varying Physical Objects Unlearnable examples: Making personal data unexploitable

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:22.135960Z

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.

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Observation 0bf3c6ee-8af0-477a-adfc-183241101d42 · outbound

This paper cites T-sea: Transfer-based self-ensemble attack on object detection.

Generalizable Targeted Data Poisoning against Varying Physical Objects T-sea: Transfer-based self-ensemble attack on object detection

Reference 16

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.121158Z

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.

source=pdf_text observed=2026-08-11T22:05:21.527280Z digest=sha256:126beb92dec24a876c4db4f6bd39ed35dbd30a3a9a0028e3fafdc65daeafc3e7

Observation 1b48dd63-c3aa-4cc5-9727-eaf10c575370 · outbound

This paper cites Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein.

Generalizable Targeted Data Poisoning against Varying Physical Objects Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein

Reference 17

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.106095Z

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.

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Observation c6f98813-cd59-427c-aebf-f347e5b9edae · outbound

This paper cites Subpopulation data poisoning attacks.

Generalizable Targeted Data Poisoning against Varying Physical Objects Subpopulation data poisoning attacks

Reference 18

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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.

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Observation e43a5f37-05c0-4644-9b91-369984dc64c7 · outbound

This paper cites Lavan: Localized and visible adversarial noise.

Generalizable Targeted Data Poisoning against Varying Physical Objects Lavan: Localized and visible adversarial noise

Reference 19

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no resolver link, observed 2026-08-11T22:05:21.539328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.539328Z digest=sha256:31e68047bfa063d715585c1acd0bec78eddea8f61ac335313ad4ee5bb1beacb2

Observation 9b555125-0be3-4d16-8298-4e6c1117dcbb · outbound

This paper cites Understanding black-box predictions via influence functions.

Generalizable Targeted Data Poisoning against Varying Physical Objects Understanding black-box predictions via influence functions

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:22.067671Z

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.

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Observation 37286e12-3ab3-466f-97a7-e4ddae664746 · outbound

This paper cites Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector.

Generalizable Targeted Data Poisoning against Varying Physical Objects Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector

Reference 21

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no resolver link, observed 2026-08-11T22:05:21.549858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 432aa5f2-f2f3-4e4c-bc4e-be6d4c8f0c03 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

Generalizable Targeted Data Poisoning against Varying Physical Objects Swin transformer v2: Scaling up capacity and resolution

Reference 22

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.052531Z

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.

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Observation bf7def1c-da81-494c-993b-f8665d3c7c5b · outbound

This paper cites Image shortcut squeezing: Countering perturbative availability poi- sons with compression.

Generalizable Targeted Data Poisoning against Varying Physical Objects Image shortcut squeezing: Countering perturbative availability poi- sons with compression

Reference 23

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raw_fallback, observed 2026-08-11T22:05:22.038768Z

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.

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Observation ab2a9a29-faa6-4764-ad7a-a150b9cfd871 · outbound

This paper cites Towards poisoning of deep learning algorithms with back-gradient optimization.

Generalizable Targeted Data Poisoning against Varying Physical Objects Towards poisoning of deep learning algorithms with back-gradient optimization

Reference 24

Resolution
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raw_fallback, observed 2026-08-11T22:05:22.024280Z

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.

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Observation c1696f21-0e41-49ae-bfcc-b5ed4b4aa41d · outbound

This paper cites Pose es- timation for category specific multiview object localization.

Generalizable Targeted Data Poisoning against Varying Physical Objects Pose es- timation for category specific multiview object localization

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:22.009403Z

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.

source=pdf_text observed=2026-08-11T22:05:21.569663Z digest=sha256:1368037cf402b069d77e5fd2f3ec4101bf19b17de4bb726912e55da9a7932189

Observation d5e26fc7-0601-490a-b46d-939bd6b5a77a · outbound

This paper cites Hidden trigger backdoor attacks, 2019.

Generalizable Targeted Data Poisoning against Varying Physical Objects Hidden trigger backdoor attacks, 2019

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.993653Z

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.

source=pdf_text observed=2026-08-11T22:05:21.574281Z digest=sha256:377da6698dff7388ab8247c0e8f031cb9181c37a0a2c1ba8b01cce2984b0f2ff

Observation 137585fa-d8b1-4fac-bd83-8d8c79923df3 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Generalizable Targeted Data Poisoning against Varying Physical Objects Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T22:05:21.578964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 48e95b7d-e6d3-4f96-b887-1c4d78cce99e · outbound

This paper cites Just how toxic is data poison- ing? a unified benchmark for backdoor and data poisoning attacks.

Generalizable Targeted Data Poisoning against Varying Physical Objects Just how toxic is data poison- ing? a unified benchmark for backdoor and data poisoning attacks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.967602Z

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.

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Observation 8fb461d7-2c8e-4d79-b72b-f8a09ae4fc9d · outbound

This paper cites Poison frogs! targeted clean-label poisoning attacks on neu- ral networks.

Generalizable Targeted Data Poisoning against Varying Physical Objects Poison frogs! targeted clean-label poisoning attacks on neu- ral networks

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.952786Z

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.

source=pdf_text observed=2026-08-11T22:05:21.589961Z digest=sha256:328f6ca83db0235d9fb5f64387771bdb75abc889d607f4611a969bf8d6e7e0f4

Observation d526eac1-6e7a-44dc-ae5e-019e1691efbc · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

Generalizable Targeted Data Poisoning against Varying Physical Objects Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.937995Z

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.

source=pdf_text observed=2026-08-11T22:05:21.594473Z digest=sha256:6d0741ebe9a7b80e5fc1bdec397b65da377c4194f286687f28d9f53858b632b4

Observation 370f5d68-49f2-4bf6-b459-36eb35b4a20e · outbound

This paper cites Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch.

Generalizable Targeted Data Poisoning against Varying Physical Objects Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.922915Z

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.

source=pdf_text observed=2026-08-11T22:05:21.599192Z digest=sha256:398f614af9f901a2b204fdf0e28dd77009420c622d6fac3489be49fe2b33bf1f

Observation 80eeaf3d-9c79-4b15-b1e7-ab7d0e5edd24 · outbound

This paper cites Cer- tified defenses for data poisoning attacks.Advances in neural information processing systems, 30, 2017.

Generalizable Targeted Data Poisoning against Varying Physical Objects Cer- tified defenses for data poisoning attacks.Advances in neural information processing systems, 30, 2017

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.907357Z

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.

source=pdf_text observed=2026-08-11T22:05:21.603490Z digest=sha256:9b621579b962b383c2fe8e50ad229b0cf7c44b0631b2e13c24d84bb2de7078d6

Observation b3cc0417-1d1b-465f-a7f7-815c11c8d3f7 · outbound

This paper cites Fooling automated surveillance cameras: adversarial patches to at- tack person detection.

Generalizable Targeted Data Poisoning against Varying Physical Objects Fooling automated surveillance cameras: adversarial patches to at- tack person detection

Reference 33

Resolution
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no resolver link, observed 2026-08-11T22:05:21.607812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.607812Z digest=sha256:4b1efa972e730853250a1b3c54928da6187a28e1acd8a08d50cf37f5b7307da0

Observation 9b2c3b6a-f0a3-42a2-a7d9-a363f7bc5701 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset, 2011.

Generalizable Targeted Data Poisoning against Varying Physical Objects The caltech-ucsd birds-200-2011 dataset, 2011

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.879335Z

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.

source=pdf_text observed=2026-08-11T22:05:21.611725Z digest=sha256:64a6af763e36fd25b604a561fadf36687aa13f8333ae1779aff8fdb68c8cca0b

Observation d2d2175e-99e7-4ded-b178-c0072dd12f10 · outbound

This paper cites Provably unlearnable data examples.

Generalizable Targeted Data Poisoning against Varying Physical Objects Provably unlearnable data examples

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.863715Z

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.

source=pdf_text observed=2026-08-11T22:05:21.615743Z digest=sha256:dd5e01e5b8157a1126da70770f385aa98c6cdedc804f5820d8437288ce7f244c

Observation 936c15cb-ce48-4ac6-a8d2-6b63e6f20d97 · outbound

This paper cites Backdoor attacks against deep learning systems in the physical world.

Generalizable Targeted Data Poisoning against Varying Physical Objects Backdoor attacks against deep learning systems in the physical world

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.848908Z

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.

source=pdf_text observed=2026-08-11T22:05:21.620459Z digest=sha256:1f645b1932f28681ee6bbc05d6bff395864bc1aa1df9097d208ff64481b9ece0

Observation ebde6ed6-cc6d-4643-b6a2-1429b1d7c53d · outbound

This paper cites Natural Backdoor Datasets.

Generalizable Targeted Data Poisoning against Varying Physical Objects Natural Backdoor Datasets

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-11T22:05:21.701243Z

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.

source=pdf_text observed=2026-08-11T22:05:21.625002Z digest=sha256:689d6418b0915b78c1e8fe66d5135a0ca1e59ec86d25ba1d07d73371ed305274

Observation 6c2c7ee5-7c99-4ce2-be1d-18e59cc925f1 · outbound

This paper cites Making an invisibility cloak: Real world adversar- ial attacks on object detectors.

Generalizable Targeted Data Poisoning against Varying Physical Objects Making an invisibility cloak: Real world adversar- ial attacks on object detectors

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.835168Z

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.

source=pdf_text observed=2026-08-11T22:05:21.630297Z digest=sha256:f995b7530741952365b5b862c97bf4898c30e954ce3159e45f56a39652f25ea5

Observation f397940c-65c3-4c3f-8e91-36c511dbd587 · outbound

This paper cites Adversarial la- bel flips attack on support vector machines.

Generalizable Targeted Data Poisoning against Varying Physical Objects Adversarial la- bel flips attack on support vector machines

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.820992Z

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.

source=pdf_text observed=2026-08-11T22:05:21.635239Z digest=sha256:d2c62d26cb6d50733f942a7765a632b99c2676fd8954fa6f0aec57c20d3b236e

Observation 529a7a72-1625-444c-8c65-3c0f04372fc7 · outbound

This paper cites Adversarial t-shirt! evading person detectors in a physical world.

Generalizable Targeted Data Poisoning against Varying Physical Objects Adversarial t-shirt! evading person detectors in a physical world

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T22:05:21.639745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:05:21.639745Z digest=sha256:3f6aa44c37c71f99d3eb5094ffc00a336a750e8f6c3fe8ba92cb679480d36e0c

Observation 091ad006-a960-4ad5-b9cb-ef50f80d4046 · outbound

This paper cites Not all poisons are created equal: Robust training against data poi- soning.

Generalizable Targeted Data Poisoning against Varying Physical Objects Not all poisons are created equal: Robust training against data poi- soning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.796817Z

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.

source=pdf_text observed=2026-08-11T22:05:21.644279Z digest=sha256:4c5e7afcb353b30d32efa1effb2bb53cda768d4cdac806f3216c42d86c23108c

Observation dd1e103f-8109-42c6-8a63-56ccd1f9d9cc · outbound

This paper cites Latent backdoor attacks on deep neural networks.

Generalizable Targeted Data Poisoning against Varying Physical Objects Latent backdoor attacks on deep neural networks

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.781129Z

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.

source=pdf_text observed=2026-08-11T22:05:21.649021Z digest=sha256:84ca61f146be614e1a2ebadcb52c3befb69e86e0b9a7faca87a45587fd99aad5

Observation c7e06f8f-7043-4fb2-8144-518a2e2e6fd9 · outbound

This paper cites Transferable clean- label poisoning attacks on deep neural nets.

Generalizable Targeted Data Poisoning against Varying Physical Objects Transferable clean- label poisoning attacks on deep neural nets

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.765244Z

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.

source=pdf_text observed=2026-08-11T22:05:21.654010Z digest=sha256:2d89975fab4b333275248c4df872f9f28e3cc17d794ad57e469bf860b7e6972a

Observation 59b3864e-ffa1-41ab-8bfd-25cc995fb013 · outbound

This paper cites sports car.

Generalizable Targeted Data Poisoning against Varying Physical Objects sports car

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:05:21.748903Z

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.

source=pdf_text observed=2026-08-11T22:05:21.658856Z digest=sha256:b17b75a8da1bf12bb5eccb13bc179e9273505f205cf2efaeaadcfaa6682759fe

Pith citing papers

No inbound Pith citation observations are available.