Paper Citation Record · LEDGER

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation

As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2501.02704.

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

pith.paper-citation-record.v1
2501.02704 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:10:36.611106Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:10:36.611106Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T22:10:36.661421Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy33
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36005ded-ced9-4dc3-9ab7-f793d30ad739 · outbound

This paper cites Attention is All you Need,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Attention is All you Need,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.506124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 03f9c932-07d5-4fe0-a85b-5a4a80c7f43e · outbound

This paper cites Language Models are Few-Shot Learners,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Language Models are Few-Shot Learners,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.369500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e507fcf3-373b-4824-b0e1-e4b34bcf8cc9 · outbound

This paper cites GPT-4 Technical Report,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation GPT-4 Technical Report,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.468724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d9b6908-4f5b-48bf-8033-0f3461d5fbf2 · outbound

This paper cites LaMDA: Language Models for Dialog Applica- tions,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation LaMDA: Language Models for Dialog Applica- tions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.447855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e21858d6-54d0-4de1-89a4-02240150e1ec · outbound

This paper cites PaLM 2 Technical Report,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation PaLM 2 Technical Report,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.422307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2616cb0-c2a3-4267-b92e-b33c026e5299 · outbound

This paper cites MLaaS: Machine Learning as a Service,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation MLaaS: Machine Learning as a Service,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.402910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 274d7a88-106d-4374-8fdb-d44d5db65bd9 · outbound

This paper cites Embedding Water- marks into Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Embedding Water- marks into Deep Neural Networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.382355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1ca1c7af-c62e-43c1-b19f-8f6c16af9715 · outbound

This paper cites Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.364368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 26383cec-7260-4216-afab-63156f3ef695 · outbound

This paper cites On the Robustness of Backdoor-based Watermarking in Deep Neural Net- works,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation On the Robustness of Backdoor-based Watermarking in Deep Neural Net- works,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.345970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29204acd-abc5-4ee2-8136-c44a9ac577ed · outbound

This paper cites REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.324844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7b29bf4c-7a02-43ed-af0e-f7c9dcca9649 · outbound

This paper cites Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.306786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e0b51e55-2e9b-4e34-8076-06e0e39a8157 · outbound

This paper cites A survey of Deep Neural Network watermarking techniques,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation A survey of Deep Neural Network watermarking techniques,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.283537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 903b8dc8-0092-43f8-b163-e5bdb69121ab · outbound

This paper cites DeepSigns: An End- to-End Watermarking Framework for Ownership Protection of Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation DeepSigns: An End- to-End Watermarking Framework for Ownership Protection of Deep Neural Networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.262844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9f8e2758-5e0c-4fe3-8de0-733330d65aa9 · outbound

This paper cites BadNets: Evaluating Backdooring Attacks on Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation BadNets: Evaluating Backdooring Attacks on Deep Neural Networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.243463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d80493aa-43bd-4861-b551-e7fa8db6fb1c · outbound

This paper cites Backdoor Learning: A Survey,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Backdoor Learning: A Survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.220777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 60521784-f4cd-4a84-aede-e25148c04538 · outbound

This paper cites Protecting Intellectual Property of Deep Neural Networks with Watermarking,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Protecting Intellectual Property of Deep Neural Networks with Watermarking,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.203573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0d7694b8-afa2-4aef-9668-50297f9393cc · outbound

This paper cites Adversarial frontier stitching for remote neural network watermarking,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Adversarial frontier stitching for remote neural network watermarking,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.185330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 301d25ad-0acf-4cca-ac91-6b36cf515ba4 · outbound

This paper cites ROWBACK: RObust Wa- termarking for neural networks using BACKdoors,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation ROWBACK: RObust Wa- termarking for neural networks using BACKdoors,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.167053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 67629d85-4fc2-44ef-877c-77c81bae6e4e · outbound

This paper cites Explaining and Harnessing Adversarial Examples,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Explaining and Harnessing Adversarial Examples,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.148020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca1f3d00-1553-4433-beec-c6f30968a825 · outbound

This paper cites Certified neural network watermarks with randomized smoothing,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Certified neural network watermarks with randomized smoothing,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.486469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45d2fdb6-9678-432e-ae77-c1f5039c1124 · outbound

This paper cites Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.117046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f9902528-f84e-4364-a90e-81937f97653d · outbound

This paper cites Entangled Watermarks as a Defense against Model Extraction,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Entangled Watermarks as a Defense against Model Extraction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.098275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b0b93197-a432-4355-9888-50854ee33d6e · outbound

This paper cites Towards Robust Model Watermark via Reducing Parametric Vulnerability,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Towards Robust Model Watermark via Reducing Parametric Vulnerability,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.079695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cfa41377-ddaa-46ce-ba78-1c6146aafed1 · outbound

This paper cites Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.061303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7893d9cf-5f04-49db-a14d-6342387032f4 · outbound

This paper cites Cosine Model Watermarking against Ensemble Distillation,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Cosine Model Watermarking against Ensemble Distillation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.040388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8ee05e90-751a-48bb-a2f1-c45c444824e0 · outbound

This paper cites Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copy- right Protection,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copy- right Protection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:37.013220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84b2ea9e-b5f4-4ec1-a247-cef03ba540c1 · outbound

This paper cites Unambiguous and High- Fidelity Backdoor Watermarking for Deep Neural Networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Unambiguous and High- Fidelity Backdoor Watermarking for Deep Neural Networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.990692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b9a0a46-d601-49dc-829e-b738675fc5b0 · outbound

This paper cites Watermarking Deep Neural Networks in Image Processing,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Watermarking Deep Neural Networks in Image Processing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.968414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f14dd3e9-0f24-412b-b938-aac4db1386c1 · outbound

This paper cites A Black-Box Watermarking Modulation for Object Detection Models,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation A Black-Box Watermarking Modulation for Object Detection Models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.935294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d299aa9-e6e6-4106-8268-934745e817fc · outbound

This paper cites Catastrophic Interference in Connec- tionist Networks: The Sequential Learning Problem,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Catastrophic Interference in Connec- tionist Networks: The Sequential Learning Problem,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.908320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c6a4b10-35e9-43f6-89b9-64a632c2d2b7 · outbound

This paper cites Compete to Compute,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Compete to Compute,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.880062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1f9f2c22-01a6-4883-9bdb-bf5cddea1f45 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Overcoming catastrophic forgetting in neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.857915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0adac29f-2bf9-4a9b-8aa2-645ded8713a7 · outbound

This paper cites Ensemble Learning in Fixed Ex- pansion Layer Networks for Mitigating Catastrophic Forgetting,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Ensemble Learning in Fixed Ex- pansion Layer Networks for Mitigating Catastrophic Forgetting,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.835911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9b968c9e-76a5-4ddf-bde7-09df024cf69a · outbound

This paper cites Measuring catastrophic forgetting in neural networks,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Measuring catastrophic forgetting in neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.810521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 306f98b3-7edd-4f6b-b08d-24892d886966 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Learning multiple layers of features from tiny images,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.579703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dfc19ac-a951-4983-a2d4-7be2f4b49f01 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Reading digits in natural images with unsupervised feature learning,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.585689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fee48e05-9d3a-45f7-9a49-e6761ec0e296 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Deep Residual Learning for Image Recognition,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.592169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091fdb3e-8871-4eff-be17-248c44bbe4f0 · outbound

This paper cites An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation An Image is Worth 16x16 Words: Trans- formers for Image Recognition at Scale,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:36.598133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 244924cd-1378-4d4d-b522-f671961044fb · outbound

This paper cites SoK: How Robust is Image Classification Deep Neural Network Watermarking?.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation SoK: How Robust is Image Classification Deep Neural Network Watermarking?

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:36.707928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ef2e456c-5436-4362-a749-323c664cf9bf · outbound

This paper cites Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation.

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation

Reference 40

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T22:10:36.670211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation ef2e456c-5436-4362-a749-323c664cf9bf · inbound