Paper Citation Record · LEDGER

Revisiting the Auxiliary Data in Backdoor Purification

As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2502.07231.

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

pith.paper-citation-record.v1
2502.07231 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:30:03.623879Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

46 of 46 outbound references displayed

verified exact4
verified fuzzy32
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e145fcf4-c693-4183-b067-5a458b967448 · outbound

This paper cites Past, present, and future of face recognition: A review.

Revisiting the Auxiliary Data in Backdoor Purification Past, present, and future of face recognition: A review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.206073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation daa4f87e-5307-4586-b804-5939b38108c3 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Revisiting the Auxiliary Data in Backdoor Purification Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.452514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa8a2b4a-92da-44ff-9ca5-96cff2e23a47 · outbound

This paper cites A new backdoor attack in cnns by training set corruption without label poisoning.

Revisiting the Auxiliary Data in Backdoor Purification A new backdoor attack in cnns by training set corruption without label poisoning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.193800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a6ddd7df-6f39-416f-a2e6-c324c764b774 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Revisiting the Auxiliary Data in Backdoor Purification Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.461944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb63554e-c083-4761-ae60-83d90077f791 · outbound

This paper cites One-shot neural backdoor erasing via adversarial weight masking.

Revisiting the Auxiliary Data in Backdoor Purification One-shot neural backdoor erasing via adversarial weight masking

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.181308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 40c921b6-ed03-48e1-8e4d-dc5cf7abc5dc · outbound

This paper cites Detecting backdoor attacks on deep neural networks by activation clustering.

Revisiting the Auxiliary Data in Backdoor Purification Detecting backdoor attacks on deep neural networks by activation clustering

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.169812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36478c83-19ab-43b8-aa03-0aaf1d17d59c · outbound

This paper cites Targeted backdoor attacks on deep learning systems using data poisoning.

Revisiting the Auxiliary Data in Backdoor Purification Targeted backdoor attacks on deep learning systems using data poisoning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.157800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3031070-5302-496e-86d3-1218e521e300 · outbound

This paper cites CINIC-10 is not ImageNet or CIFAR-10.

Revisiting the Auxiliary Data in Backdoor Purification CINIC-10 is not ImageNet or CIFAR-10

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.478578Z

Source-reported events for the cited work

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Observation 120f2420-d0f7-4da3-ad19-ca97ec43e97c · outbound

This paper cites Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies.

Revisiting the Auxiliary Data in Backdoor Purification Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:30:03.726659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c14bd710-40db-4d2c-94e9-9d19b86db87b · outbound

This paper cites Badnets: Evaluating backdooring attacks on deep neural networks.

Revisiting the Auxiliary Data in Backdoor Purification Badnets: Evaluating backdooring attacks on deep neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.145946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11d5899f-cbf6-478d-a1ea-b4e112f9d745 · outbound

This paper cites Deep residual learning for image recognition.

Revisiting the Auxiliary Data in Backdoor Purification Deep residual learning for image recognition

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.490419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20e936c8-5685-46f3-b025-3e201112fff1 · outbound

This paper cites Identity mappings in deep residual networks.

Revisiting the Auxiliary Data in Backdoor Purification Identity mappings in deep residual networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.494448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74a91be4-4139-49c4-9cbb-53bb035ba06f · outbound

This paper cites Denoising diffusion probabilistic models.

Revisiting the Auxiliary Data in Backdoor Purification Denoising diffusion probabilistic models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.498023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c342554-6767-47a3-a560-5257fbce2eec · outbound

This paper cites Revisiting data- free knowledge distillation with poisoned teachers.

Revisiting the Auxiliary Data in Backdoor Purification Revisiting data- free knowledge distillation with poisoned teachers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.111889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 69859d76-a513-4bfb-a770-6fc81021499c · outbound

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

Revisiting the Auxiliary Data in Backdoor Purification Learning multiple layers of features from tiny images

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.505650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d4f3218-11b8-4f64-a175-45bc7097ae73 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Revisiting the Auxiliary Data in Backdoor Purification Tiny imagenet visual recognition challenge

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.092051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64adac47-dc99-435c-b7f2-5dc3d3a36faa · outbound

This paper cites Neural attention distillation: Erasing backdoor triggers from deep neural networks.

Revisiting the Auxiliary Data in Backdoor Purification Neural attention distillation: Erasing backdoor triggers from deep neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.080441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2ed2dfe-ce07-4c49-aafe-5bfe9c09fbf0 · outbound

This paper cites Reconstructive neuron pruning for backdoor defense.

Revisiting the Auxiliary Data in Backdoor Purification Reconstructive neuron pruning for backdoor defense

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.067647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 160ab874-1063-4108-aa2f-572ee11ee73a · outbound

This paper cites Invisible backdoor attack with sample-specific triggers.

Revisiting the Auxiliary Data in Backdoor Purification Invisible backdoor attack with sample-specific triggers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.054665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d734157-2f59-4ed9-a2f0-71194e7d5945 · outbound

This paper cites Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation.

Revisiting the Auxiliary Data in Backdoor Purification Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:30:03.710465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d134961a-5f8f-4c10-871d-fb365374c80f · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks.

Revisiting the Auxiliary Data in Backdoor Purification Fine-pruning: Defending against backdooring attacks on deep neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.042413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 317fcc12-153e-4fda-a3d1-523f29af8aa5 · outbound

This paper cites Computing systems for autonomous driving: State of the art and challenges.

Revisiting the Auxiliary Data in Backdoor Purification Computing systems for autonomous driving: State of the art and challenges

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.028526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 204ace4a-d1c8-45be-a722-bb9511b29e50 · outbound

This paper cites Towards Stable Backdoor Purification through Feature Shift Tuning.

Revisiting the Auxiliary Data in Backdoor Purification Towards Stable Backdoor Purification through Feature Shift Tuning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.536459Z

Source-reported events for the cited work

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Observation 5659dd52-bc61-423a-9703-a381d93de444 · outbound

This paper cites Input-aware dynamic backdoor attack.

Revisiting the Auxiliary Data in Backdoor Purification Input-aware dynamic backdoor attack

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:04.014227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c06fd3d1-789b-49d6-8816-ac679eaecf8f · outbound

This paper cites Wanet - imperceptible warping-based backdoor attack.

Revisiting the Auxiliary Data in Backdoor Purification Wanet - imperceptible warping-based backdoor attack

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.999614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed38cf0f-a209-4507-aafa-07872a3f11bf · outbound

This paper cites Imagenet large scale visual recognition challenge.

Revisiting the Auxiliary Data in Backdoor Purification Imagenet large scale visual recognition challenge

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.547936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6758f524-e135-4270-ac5c-7f9c74a220b5 · outbound

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

Revisiting the Auxiliary Data in Backdoor Purification Poison frogs! targeted clean-label poisoning attacks on neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.978957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11763ac9-c440-4b5a-8bc1-dd0abc925ee1 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Revisiting the Auxiliary Data in Backdoor Purification Very deep convolutional networks for large-scale image recognition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T13:30:03.555621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb033342-5c97-4ce5-81eb-88e5a17dbeb5 · outbound

This paper cites Deep perturbation learning: enhanc- ing the network performance via image perturbations.

Revisiting the Auxiliary Data in Backdoor Purification Deep perturbation learning: enhanc- ing the network performance via image perturbations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.959917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6bf342a-0984-4d6f-a7f8-0f5cf839f096 · outbound

This paper cites The german traffic sign recognition benchmark: a multi-class classification competition.

Revisiting the Auxiliary Data in Backdoor Purification The german traffic sign recognition benchmark: a multi-class classification competition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.948024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfe19ca4-db12-4b0b-a473-39e253514113 · outbound

This paper cites Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation.

Revisiting the Auxiliary Data in Backdoor Purification Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.936027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 07fa6fc9-0cdf-40f0-8648-d5900054e0cb · outbound

This paper cites Neural cleanse: Identifying and mitigating backdoor attacks in neural networks.

Revisiting the Auxiliary Data in Backdoor Purification Neural cleanse: Identifying and mitigating backdoor attacks in neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.924077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6839fdd8-e683-4e55-8e4e-63bbf009ce2b · outbound

This paper cites Shared adversarial unlearn- ing: Backdoor mitigation by unlearning shared adversarial examples.

Revisiting the Auxiliary Data in Backdoor Purification Shared adversarial unlearn- ing: Backdoor mitigation by unlearning shared adversarial examples

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.912017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d342351c-4a0c-46f1-b0ef-967d83f5396b · outbound

This paper cites Backdoor Mitigation by Distance-Driven Detoxification.

Revisiting the Auxiliary Data in Backdoor Purification Backdoor Mitigation by Distance-Driven Detoxification

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:30:03.680953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc71d9c8-7251-4c7d-935b-e8536b4e0b85 · outbound

This paper cites Backdoorbench: A comprehensive benchmark of backdoor learning.

Revisiting the Auxiliary Data in Backdoor Purification Backdoorbench: A comprehensive benchmark of backdoor learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.899616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 308b2868-35f2-44ca-b2b9-144311a148aa · outbound

This paper cites Defenses in adversarial machine learning: A survey.

Revisiting the Auxiliary Data in Backdoor Purification Defenses in adversarial machine learning: A survey

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.886477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9079c694-02c2-4b7c-95e0-522b236a4ee2 · outbound

This paper cites Backdoorbench: A comprehensive benchmark and analysis of backdoor learning.

Revisiting the Auxiliary Data in Backdoor Purification Backdoorbench: A comprehensive benchmark and analysis of backdoor learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.874038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70f40f96-c42c-4321-8a2a-5e203d54030c · outbound

This paper cites Adversarial neuron pruning purifies backdoored deep models.

Revisiting the Auxiliary Data in Backdoor Purification Adversarial neuron pruning purifies backdoored deep models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.862802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a744f811-0457-4507-9d36-bcf4b904d6c7 · outbound

This paper cites Spatially transformed adversarial examples.

Revisiting the Auxiliary Data in Backdoor Purification Spatially transformed adversarial examples

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.851707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e158f87-36dc-47a1-a107-af261f436269 · outbound

This paper cites Morley Mao, and Ruoxi Jia.

Revisiting the Auxiliary Data in Backdoor Purification Morley Mao, and Ruoxi Jia

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.839214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6478dc97-5aee-480e-8130-919607e0eed6 · outbound

This paper cites Adversarial unlearning of backdoors via implicit hypergradient.

Revisiting the Auxiliary Data in Backdoor Purification Adversarial unlearning of backdoors via implicit hypergradient

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.826589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd6ecc63-9417-4b1d-9dd7-b0a6b11f1700 · outbound

This paper cites How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?.

Revisiting the Auxiliary Data in Backdoor Purification How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-08T13:30:03.662611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f6cafadd-f1bd-418d-9b75-d228edd7ed3c · outbound

This paper cites Data-free backdoor removal based on channel lipschitzness.

Revisiting the Auxiliary Data in Backdoor Purification Data-free backdoor removal based on channel lipschitzness

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.814129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ace98698-b558-4d6f-bb32-13b9ea15a6fc · outbound

This paper cites Enhancing fine-tuning based backdoor defense with sharpness-aware minimization.

Revisiting the Auxiliary Data in Backdoor Purification Enhancing fine-tuning based backdoor defense with sharpness-aware minimization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.802195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 452e2663-6d1f-4e28-8ef9-42aade042626 · outbound

This paper cites Neural polarizer: A lightweight and effective backdoor defense via purifying poisoned features.

Revisiting the Auxiliary Data in Backdoor Purification Neural polarizer: A lightweight and effective backdoor defense via purifying poisoned features

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.790109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 793ff486-4d91-47e7-9d83-a434097f2214 · outbound

This paper cites Vdc: Versatile data cleanser for detecting dirty samples via visual-linguistic inconsistency.

Revisiting the Auxiliary Data in Backdoor Purification Vdc: Versatile data cleanser for detecting dirty samples via visual-linguistic inconsistency

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:30:03.777891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.