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Paper Citation Record · LEDGER

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

As of 23 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 2026-08-23T06:30:58.430688+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=sha256:9c684264a5799678497979fb22e91c114fe9b6b41af2f87811e94470f4a9ef08

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.781600Z digest=sha256:a5fb10c5fb280b172a0aa474ea0bfc4a94684982cf6aa00af7ea015de675762a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.785784Z digest=sha256:d2865cd49cb2e69ab2b4b8b190f05cfe80c2bb7c6c028dc89b85c1aecd1c01ba

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.790144Z digest=sha256:bfb2162d792bab985e29f112f74d9e19fccf9c082cd779705794ea9535bf4809

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.794483Z digest=sha256:675892aabb75bf67dd0c38e48628b718f2a7715e7312a0926d3229fcc0108a83

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.807142Z digest=sha256:26f207698b77f9b0c635c7a009048c6ec9dd4c65e6875984bc26f4d97ad015de

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:af96e07e873f7d6e12234dd93f0a0b436ce20a2695ff3962a12cfc000ffed960

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=sha256:92cdf02556c0ca2eb2bdb37cdc02c3146442824bed8edf087eaef72190e6de56

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.829148Z digest=sha256:560860ec00a3352beca045fc3ef0dd1f6f19b68cc1240d026ea0c4dff75b35e7

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=sha256:6145a1689c4b5e685488045b9fee243f3340b6c3202a31c0e8f129a95c4f51ef

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:defe930d84ac9785a3e89bbbb2bafcd9d173f57a4449a17e4e8d671479bc8404

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

Resolution
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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.868517Z digest=sha256:d45df754a357aee54c39704ce817b1bf51d5ff7ce7f279a197f89e125c2981fe

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.872073Z digest=sha256:97fb3ee010fafc9794caba4d34b9054e488deb1d0a69031ca734f0e4e582778a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.875486Z digest=sha256:4261f635735f8fc28b469946211e8be6a3d8877e0724046eeadc385e3aec39e3

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.879275Z digest=sha256:4f927ad9af0ab7d81b4fcbb183776f2ccd5861b7da4bc09c8e47e86f637546d9

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.883742Z digest=sha256:a0cd2384e61bd4fe24019097d70e3d572ddbf85d70300128b97d42938bb5a146

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.886987Z digest=sha256:81ad2d868f73a4d84262858923687b23d56ebbc6f6822cecfdf7b4806f656c35

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.890250Z digest=sha256:fa013af8278995e9cd29c144151c214d5812cfbc9e69ffb10e3ff1990f30ed60

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.893821Z digest=sha256:5ff57bc943ed03f5b441f991f562e219ab9bbd2768ddfba84f3be37fd4d3eb79

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:a48a41f38a70881dd4ed34dfd22f8e6428513a3b27fbcc56b756177df5779f7a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.900033Z digest=sha256:c6fe481791c5b497f73749b226e6d10c6780b155b0edb0ab58de321c34962357

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.903793Z digest=sha256:3df73f21dc140fb37891ebc4fc3058ecbc24db5e968cf8ff93edd5f214b2d262

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.907750Z digest=sha256:6b00ba6f0ef07306f3f52c4aafff90c18eed9da54feef318f15d9dc201b1f655

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.910807Z digest=sha256:6e96b35724f81c2b8710102c4743805e25623344fe7272458b376e81f1029527

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.914260Z digest=sha256:b8f0138c5cb9f648e65ed94bc6613c0cd3cdcfa0c5cf931a87444fd34b0ec85c

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.917250Z digest=sha256:e4b93465b9d46a4415be9f529d3113737a1762bd75b47f08b219f4f3335d4dc6

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.920139Z digest=sha256:f908fd33056e64925bd516975f8233e8a817b8204348c2ca2aaa4cdf392ee7ba

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.924159Z digest=sha256:d190065f4a3dae40a09ec392d133d90c592b38f48dee1481e21b4504384d222b

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.928702Z digest=sha256:6afbd44643c03a2855ec5a09ee44b9a6761e3eff88af9f5355b297ad2b749942

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.932695Z digest=sha256:ff3ef009173995ce5fdf081bf42ca0162f9e1714f306306f3cde8eb26baf4e21

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.936460Z digest=sha256:41152498a3203d3a1c31b70cd3797316199f9345ef0766479466886105b7fca1

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.940408Z digest=sha256:cee899054a1e495cf64999fb2cbc65eb822a364333d9ef1eb298542ee4425f14

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.944227Z digest=sha256:36b264fc0bf2a684c1ed79d58cbe68900aeb2ab724d41bed59c4dd28d42c3d37

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.

source=pdf_text observed=2026-08-14T15:43:28.948193Z digest=sha256:1b802e2ff08f30a72e4a1159abe13e4947234a2ca4cf5525537b7e03f626c170

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.951741Z digest=sha256:4424105dedf93cb3df9de4e1b434270c41162b6179cf6c7464779f03e6bb5fc0

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=sha256:5e52433e4aabb7abceb9c46210d2e946fb17449746cb4bfe95e713d70da2c2f0

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.958516Z digest=sha256:a775a81c0d7044b7ca5cc139a39326218cf90ddea4170acf9a7b44a5bd967c54

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.961960Z digest=sha256:22eaf2658decfaa1d2f9bd5f4d89e648e23f169bb3855e1f594d2c3de68794da

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.965075Z digest=sha256:acf3425eedc37cd0dd84d49cb60d327471a801e3cf0eaf32bb977b8c460d5d00

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:28.968192Z digest=sha256:b8207e79c60d146f447d91b4a932550773d95b84eae6dd483d4e44af1f47cb92

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=sha256:31f94bd1a570fe2e3dc28c72fc6a2ceebd5782b18a04e7c21dd4d69d23ad768b

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:29.075987Z digest=sha256:10110f3a7bb87bf00e664a131ef0215daeace3e3b10471c0ed9c2f4198c9d392

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:29.131922Z digest=sha256:ea75fc7555076bf03c785f71a5549d6d31b47b46243c91d9a544e30fbdb56df5

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:29.135952Z digest=sha256:27bf73d0fd1bdd7f792f78c54f1cadc0ab372d142965bbdaa7ae84d6b4892470

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:29.139309Z digest=sha256:365b894aa8c10c7ac7f7eb549ef9da963e47a18a9a7bf30a10162dc87c1d2f34

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T15:43:29.142715Z digest=sha256:23cd4bdf8e5edfd95ab70b6bf7393bd3843faffb83bea6b05f29d1795407246a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T04:45:56.542435Z digest=sha256:0621e8e54be874faca83b94cb3a39ca35cf0ba6fe7ffd03593d8c7484a5ee470