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Paper Citation Record · LEDGER

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection

As of 16 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:1908.00686.

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

pith.paper-citation-record.v1
1908.00686 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:43:29.142715Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:45:56.542435Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T04:45:56.637195Z

Reference resolution

46 of 46 outbound references displayed

  • verified exact0
  • verified fuzzy35
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63d19641-51bd-437d-ace9-55e163a0bce8 · outbound

This paper cites How To Backdoor Federated Learning.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection How To Backdoor Federated Learning

Reference 1

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unresolved
no resolver link, observed 2026-08-14T15:43:28.776675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.776675Z digest=sha256:4ed4478c727a39486dce0267c83d507bc44c2792f0bb58a9bc33c46f77a826c3

Observation 7cfdc3af-6827-4753-94da-3841ba01c7c1 · outbound

This paper cites Mitigating poisoning attacks on machine learning models: A data provenance based ap- proach.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Mitigating poisoning attacks on machine learning models: A data provenance based ap- proach

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.821814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.781600Z digest=sha256:40e88fcc2c4c56c869f42657696832ec324ff78e635a12b7e6cf4198f040fb53

Observation f431349e-0de9-4a77-aceb-eb9ccbd4516d · outbound

This paper cites Statistics for experimenters, volume 664.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Statistics for experimenters, volume 664

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.641689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.785784Z digest=sha256:848dcff1ae3e25919dbbf24ff996fdd8fbe7c483ae7dd4bcd36a2b1bafe3fae0

Observation f6e16c9a-7050-478e-8f33-63d7a7642f5e · outbound

This paper cites Detecting backdoor at- tacks on deep neural networks by activation clustering.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Detecting backdoor at- tacks on deep neural networks by activation clustering

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.518235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.790144Z digest=sha256:63a2c96c39a2b9a54ed22d087b29669ee58271cd657d2c776772398a313b36d7

Observation 0c47f921-e306-4a90-b285-1c65fa602e3b · outbound

This paper cites Bayesian face revisited: A joint formulation.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Bayesian face revisited: A joint formulation

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.505707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.794483Z digest=sha256:d65d66875e092d9dad32d69b434fdc24aa1e5c85cd4e5f78684ec88e77e2e251

Observation 6c5138b2-9db5-4650-b9dd-0f1ccdf0459e · outbound

This paper cites ZOO: zeroth order optimization based black-box attacks to deep neural networks with- out training substitute models.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection ZOO: zeroth order optimization based black-box attacks to deep neural networks with- out training substitute models

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.493546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.807142Z digest=sha256:c2729efc7877f108b28feb6ca8671a8c1f65ed5b4eed660b86e1025aa4b5da1c

Observation ca9e7189-01a1-453b-81c1-d2483eb7a51d · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 7

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unresolved
no resolver link, observed 2026-08-14T15:43:28.813256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.813256Z digest=sha256:915a55ef08df21d9b12dbcb7ec38f4a24ef6a80e63fcf3e818438f0fa20d3a8a

Observation 9b017ecd-6b23-4124-9b26-abd69b0e26b7 · outbound

This paper cites SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T15:43:28.817605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.817605Z digest=sha256:0babccd75dc0f616251ad5997da1b2c80b1d3849fe066705495c0064c179afe8

Observation ab0e4f90-ff4d-4fce-8cb0-b30b421b82ae · outbound

This paper cites STRIP: a defence against trojan attacks on deep neural networks.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection STRIP: a defence against trojan attacks on deep neural networks

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.481293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.829148Z digest=sha256:e739e58376c5baba55e679f566fffac55361d5e3174aae4cf760a2ceabf80b26

Observation 7e1fdca4-5857-4876-a226-677eb74ec238 · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 10

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unresolved
no resolver link, observed 2026-08-14T15:43:28.840933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.840933Z digest=sha256:3b4ea9e04cf36f537f547cc7ecc74be2fcc37557c7196d30f61d54361bf055f6

Observation 4301301b-287b-48b9-a199-3ab991d95d60 · outbound

This paper cites Deep residual learning for image recognition.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Deep residual learning for image recognition

Reference 11

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unresolved
no resolver link, observed 2026-08-14T15:43:28.853963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.853963Z digest=sha256:9373b641741570794796aab70ccba335711743ae944399f7369c37b86309f322

Observation fdd595dd-d2f9-4881-947b-aa961da973d3 · outbound

This paper cites Hodge and Jim Austin.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Hodge and Jim Austin

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.454854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.868517Z digest=sha256:73d4ec55587a1d82c2883fc6b50b97d5d4f70a4e5f232535c65690b3dbe71260

Observation 6c0562bc-7486-4450-855e-e29ee7324ce5 · outbound

This paper cites Black-box adversarial attacks with limited queries and information.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Black-box adversarial attacks with limited queries and information

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.444424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.872073Z digest=sha256:8222d3856fbef03f568deb93d13fc32076b0de6f4264df3544b380b54c0e2abb

Observation f9e6f3be-83a5-4a47-9fbe-30537e595372 · outbound

This paper cites Algorithms for mining distancebased outliers in large datasets.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Algorithms for mining distancebased outliers in large datasets

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.369717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.875486Z digest=sha256:8f1be1bd647cd7c235d06044b08b8378d48c664cda44bc1cbcdc3d2e45a6bee3

Observation 725bb6ef-d60a-4373-ae2e-1b0af3b327ec · outbound

This paper cites Parameter estimation and hypothesis testing in linear models.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Parameter estimation and hypothesis testing in linear models

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.249569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.879275Z digest=sha256:f2a7a4e699cc44ca0046142f7cb500d84f1f621b7901693d29fbf21fbdd88972

Observation a9d54593-1a7f-432b-ae1c-d5aab8abdcc9 · outbound

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

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Understanding black- box predictions via influence functions

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.141092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.883742Z digest=sha256:a3f9b34ee3ee737eb128f829cb6fe8f5e036c547000219f4aff659fe905812a6

Observation 2936a901-ac3b-47cf-8891-1b273c33a244 · outbound

This paper cites Quantifiable data mining using principal component analysis.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Quantifiable data mining using principal component analysis

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.129534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.886987Z digest=sha256:e7c6bebe8c45351dc6c3f34cf618bb50e855e9730eb8095f0ae12c04e81d7c8f

Observation b218ac15-7eb3-4b12-963c-85c227f596d5 · outbound

This paper cites Learning multi- ple layers of features from tiny images.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Learning multi- ple layers of features from tiny images

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.118289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.890250Z digest=sha256:04f06e20f83c1ed3702c5be4dfded150e1268ee38eec82695aa7d63b2b9da8b3

Observation 4c080cc9-a1dd-48d0-a7eb-f02b9c6c329e · outbound

This paper cites Gradient-based learning applied to document recognition.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Gradient-based learning applied to document recognition

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.107220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.893821Z digest=sha256:fd8e4877251b0d517150b2f599fb4faf7c60325cc2430ac638545775d4b13d46

Observation 56a03e3a-a9ae-4eec-b9d1-c47b8e1855b0 · outbound

This paper cites Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-14T15:43:28.896968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.896968Z digest=sha256:a69b6a8e18cbafa169c923e55c0d47aeeffce379dc039dfd9623583742e465a4

Observation 99b2737d-6b24-48d1-8c26-2fde07520d50 · outbound

This paper cites Printracker: Fingerprinting 3d printers using commodity scanners.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Printracker: Fingerprinting 3d printers using commodity scanners

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.088857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.900033Z digest=sha256:f0f1a6054a849d05bda8e2a60868014dd9ff640bb4cb7eb533963d66614b1506

Observation 0cc2da76-2946-4538-8f73-302fecf02ff8 · outbound

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

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Fine-pruning: Defending against backdooring attacks on deep neural networks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.077838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.903793Z digest=sha256:87a479abc3b6b467f907b5d46aed1c97df53bf20409dc14410bdca952a861efc

Observation 621b5f87-806e-441c-8b6b-04460faa57ab · outbound

This paper cites ABS: scan- ning neural networks for back-doors by artificial brain stimulation.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection ABS: scan- ning neural networks for back-doors by artificial brain stimulation

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.067185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.907750Z digest=sha256:3a447c0512f7eac3d631e6965da791ad89484b76d79aa6249e1334ec4b7eda7a

Observation 1a7a2155-497c-47ee-9b95-d9bc78a73043 · outbound

This paper cites Trojaning attack on neural networks.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Trojaning attack on neural networks

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.056525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.910807Z digest=sha256:1723a149a68c0340c2d4be6fba94ff2e18c9b784276cdadd10af76eb93fbecc8

Observation 4d2a2d6f-1b1e-4dda-9986-510b8c45d999 · outbound

This paper cites Fisher dis- criminant analysis with kernels.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Fisher dis- criminant analysis with kernels

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:30.045629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.914260Z digest=sha256:12b375071557c0167bd1977de20b89312d09d7adf7e9ebc6301af569c98bd1ec

Observation aeba5016-e084-4ce7-8db0-8a84523551af · outbound

This paper cites A system for the analysis of jet engine vibration data.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection A system for the analysis of jet engine vibration data

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.895572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.917250Z digest=sha256:5d765c0b17a32b97906672046d794a841f4ce1b46f12d6e9200025ebd462c6e1

Observation be374c72-6ba2-40cf-be01-00850f335d43 · outbound

This paper cites Level playing field for million scale face recognition.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Level playing field for million scale face recognition

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.757635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.920139Z digest=sha256:edea262b3c679bbbf53682a47314e3b0254614b825af06b057967f366d17ab6f

Observation e3918918-7354-4493-86d6-1d4e3bdb3ba9 · outbound

This paper cites Misleading learn- ers: Co-opting your spam filter.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Misleading learn- ers: Co-opting your spam filter

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.646730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.924159Z digest=sha256:b70555ce4326ad1b96ffec5aa3f052baa71dc0ef1918aea5c77a16782f754705

Observation 5465e3a4-0625-4046-a7c2-6f72211d1fdb · outbound

This paper cites A data-driven ap- proach to cleaning large face datasets.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection A data-driven ap- proach to cleaning large face datasets

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.635352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.928702Z digest=sha256:1bd0acee894b685206fdd522b05547dd7bc2a2312fc902001e651370596ea1ba

Observation f63ffce7-8ddd-4fb3-bdb9-5710f2659079 · outbound

This paper cites Defending neu- ral backdoors via generative distribution modeling.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Defending neu- ral backdoors via generative distribution modeling

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.623772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.932695Z digest=sha256:038186dd7786fbae8e76af39d14c7b6427b61dcc71fdc335ddf8533340c21055

Observation c8e63038-70f2-4664-8b57-c7f06d8fd679 · outbound

This paper cites Berg, and Li Fei-Fei.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Berg, and Li Fei-Fei

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.612425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.936460Z digest=sha256:40b95b82f3b1efc4b0ba6390f2d2d40383768d3c56195953bbcb4e7bfe3eb62d

Observation de6856a4-fdec-40d5-bcd7-32907353f21a · outbound

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

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Poison frogs! targeted clean-label poisoning at- tacks on neural networks

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.600100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.940408Z digest=sha256:f6fea73725bb642a8822a52969299238e5a68175a8341a0d67fe4c0fe682334a

Observation 1ea6727f-0d98-4c4e-94de-d43233abf413 · outbound

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

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Very deep con- volutional networks for large-scale image recognition

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.588515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.944227Z digest=sha256:5e359101d53e05ac5d392c2397037c29ab3b10f42f03153ef363e66d4cfa62b9

Observation 1f1ccc03-efb4-4d31-93ef-5eaec9819a69 · outbound

This paper cites DARTS: Deceiving Autonomous Cars with Toxic Signs.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection DARTS: Deceiving Autonomous Cars with Toxic Signs

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-14T15:43:28.948193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3df7f62f-5a00-4765-86cf-7b89b4572821 · outbound

This paper cites an unresolved cited work.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-14T15:43:29.537742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation df83ce90-cf8e-4732-9c9a-72ae398e82e6 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-14T15:43:28.955200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:28.955200Z digest=sha256:e1fad90f32db88f004d84cbe2191a73b47c87a9835e8c08e216de73227c574e8

Observation 54428433-67b8-46a6-8439-6f06faf43edc · outbound

This paper cites Going deeper with convolutions.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Going deeper with convolutions

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.374017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.958516Z digest=sha256:283453a73f8b66b55d97e3ef3394a50566ea4e7885c68b1af22b8434a9c2673f

Observation 0e66835c-aa9f-49de-91ad-1393877b1b77 · outbound

This paper cites Goodfellow, and Rob Fergus.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Goodfellow, and Rob Fergus

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.318951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.961960Z digest=sha256:622b99715dc2c14007051bf4b720f12e032cb1dd1c4746e9b12f3240d8d0c42e

Observation 6f26f573-bcdd-4fb7-a774-ba2813b735dc · outbound

This paper cites Deep learning approach for network intrusion detection in software de- fined networking.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Deep learning approach for network intrusion detection in software de- fined networking

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.308409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.965075Z digest=sha256:d37a7f542e7649136ab373987369801c2de5a44214308aa358404a6cfb14e339

Observation ded39325-bb96-43f0-9752-02a6b0c73b87 · outbound

This paper cites Spectral signatures in backdoor attacks.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Spectral signatures in backdoor attacks

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.297222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:28.968192Z digest=sha256:4bf13f1cb4dd703e0484e73691cc54d103d12094274532a73c2d75602b094da9

Observation a2754a0b-ca1d-4053-b73d-7bef7a3fe521 · outbound

This paper cites NNoculation: Catching BadNets in the Wild.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection NNoculation: Catching BadNets in the Wild

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-14T15:43:29.001856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:43:29.001856Z digest=sha256:e7da7f49b39176d45cadc73a6627c72cea8f10bd7ac989d6e0027b8d23e3c439

Observation 128692de-3b95-485b-b14a-1b7f9e9ea676 · outbound

This paper cites an unresolved cited work.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-14T15:43:29.285100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:29.075987Z digest=sha256:39b5b3cc6d25e3a1dc627f336ff942a65cfa792776ed28f1b39a1ca3c2abe940

Observation 2107d3ff-efbb-4f91-9684-5a6b458d7e99 · outbound

This paper cites Adversary resistant deep neural networks with an appli- cation to malware detection.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Adversary resistant deep neural networks with an appli- cation to malware detection

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.273032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:29.131922Z digest=sha256:eff374b8d7dab62e4fb4f37114d5ec7f5b33b0f0492b3ab2a385410fbe6ff58d

Observation 3dd8b4e6-1f0b-4900-aff6-681cbfde6f4f · outbound

This paper cites A unified framework for subspace face recognition.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection A unified framework for subspace face recognition

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.262441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:29.135952Z digest=sha256:6c70c54de021a8200362da6dd6929a1d2315c0c30c3ad030fd162a1043e59311

Observation 58d76a00-30b9-459d-9cca-4ee5e2155a40 · outbound

This paper cites Chi-squared distribution — Wikipedia, the free encyclopedia, 2019.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Chi-squared distribution — Wikipedia, the free encyclopedia, 2019

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.251704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:29.139309Z digest=sha256:a0ab070af9f15058553e166691df34a2c7580aa8c3809f36a4bd9c1387e1e6ec

Observation 33aec0b2-3691-4d80-9d62-496f3364f49c · outbound

This paper cites The large-sample distribution of the likelihood ratio for testing composite hypotheses.

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection The large-sample distribution of the likelihood ratio for testing composite hypotheses

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:43:29.238147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:43:29.142715Z digest=sha256:10250349068e9ba92305e6574339f192d5840ac5dc31041bfe1637788a68cdca

Pith citing papers

Observation c4be214c-0349-434e-a2df-13ae7eb2da79 · inbound

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization cites this paper.

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-14T04:45:56.644346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T04:45:56.542435Z digest=sha256:3b9e3debc296b6b79d64a4878669dd4e3ee62b84cf3fae02e0a87d42a0a4783b