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Paper Citation Record · LEDGER

Generalizable Targeted Data Poisoning against Varying Physical Objects

As of 12 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 2026-08-12T06:34:41.77262+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

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  • verified fuzzy34
  • unresolved9
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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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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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 2026-08-12T06:34:41.77262+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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

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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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.

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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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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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.

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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
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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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.527280Z digest=sha256:e6318fde83754d2ec6ec8f772177ae147b51a8ab67f13247bc800caf6b3be59e

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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 2026-08-12T06:34:41.77262+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

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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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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

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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 2026-08-12T06:34:41.77262+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

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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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.569663Z digest=sha256:999770eb81737c8f10d86ad4c41d4ef70ea9e07c1bfe6964d34d424520906cc2

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 2026-08-12T06:34:41.77262+00:00.

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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.

source=pdf_text observed=2026-08-11T22:05:21.578964Z digest=sha256:57d9fb2a49869c73f03d2af1003d46150fd83b7e3d6dc9ad8999e5f8f15517db

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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.589961Z digest=sha256:3b173328ac43c0fb5b4b73e17c031079ee1b6ed14d38ee6e5aca70a4b5900d1f

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.594473Z digest=sha256:31e447c26ea328050d99c8c11c8ea90da53f52263b1c6734e85c07c98313aede

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.599192Z digest=sha256:7bdd1f063a11cf7379a963242309bc3869ef70f90b01c64fe259131b0a3814f6

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

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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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.603490Z digest=sha256:1eba4c70467e34f345917c5e6cd0d0347421783b4e6e515f3bcfd4033b81d777

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

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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

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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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.611725Z digest=sha256:1edba0bca410548b6a576b5ac97fc18d3e4f53aff88d1ad8104b7bd9bce02a75

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.615743Z digest=sha256:e48219778b34b27935bcb81da27596bbf1672dfd2571698804317b63ae918699

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.620459Z digest=sha256:fedf621af7214986170c8d42d48f8e8cbbab8ef057fca2a9efcd31f1035ff539

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.625002Z digest=sha256:82e17532f8e749d324c392ab461528686134e3641689cffd32234b0f0688f09d

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.630297Z digest=sha256:66dfab18838d94dddbc5782e1e2fab06d2220a1cd898fa2276f478033b9b5173

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.635239Z digest=sha256:78e57c0f97ba44d09d8a4e489ade5c4d1ddf01542fe9735606a90e65e80847c4

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.644279Z digest=sha256:6441b7855a4c502da74b5231ded6bb169209a37de191a158d9f6ad2f34900f0c

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.649021Z digest=sha256:f335d8e006640a7b89778c6cb07fd2d4e1391fbf0bc0e09f7831761b06acd99a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.654010Z digest=sha256:7a47cc78adeafe969fc7e6a2cca89aa7f2f6c56c13bb7bdecbe1862ba34df5d1

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T22:05:21.658856Z digest=sha256:2f3a0b6d6f3b0bbe3f4f63f615738b3204fcbfa1135d0ab45007ff7bb336ebf5

Pith citing papers

No inbound Pith citation observations are available.