Paper Citation Record · LEDGER

Deep Learning Model Security: Threats and Defenses

As of 18 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 2 inbound Pith citation observations for arXiv:2412.08969.

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

pith.paper-citation-record.v1
2412.08969 v2

Coverage vector

measured 84 of 84 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:24:26.108033Z

measured 86 of 86 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:36:18.728469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T09:54:34.401614Z

Reference resolution

84 of 84 outbound references displayed

verified exact7
verified fuzzy42
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 273ab71b-86fb-4aec-bef0-30766f9e2155 · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Deep Learning Model Security: Threats and Defenses Deep learning.nature, 521(7553):436–444, 2015

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.579907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0905f6e-d753-4a66-ad17-8d64aae5f004 · outbound

This paper cites MIT Press, 2016.

Deep Learning Model Security: Threats and Defenses MIT Press, 2016

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.588417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a522030-018c-4a7c-8904-4c7af03e3dd3 · outbound

This paper cites Deep learning , volume 1.

Deep Learning Model Security: Threats and Defenses Deep learning , volume 1

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.595377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68974850-b6c9-49ed-9d12-7f66955e8c58 · outbound

This paper cites Atoms of recognition in human and computer vision.

Deep Learning Model Security: Threats and Defenses Atoms of recognition in human and computer vision

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.602833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5023a38-2e5b-41ec-b220-3d03faf11b11 · outbound

This paper cites Understanding natural language.

Deep Learning Model Security: Threats and Defenses Understanding natural language

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.610812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e731c4c-f7a4-4e6d-b88b-0ba45973cb19 · outbound

This paper cites Playing games.

Deep Learning Model Security: Threats and Defenses Playing games

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.184170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7339f096-c800-4900-b07e-11090898c4e7 · outbound

This paper cites An introduction to Python.

Deep Learning Model Security: Threats and Defenses An introduction to Python

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.163277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 503b16a4-5358-48db-b99b-d5dd1cf132e3 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Deep Learning Model Security: Threats and Defenses Pytorch: An imperative style, high-performance deep learning library

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.648675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dece865-78eb-4b45-bcc3-06b67b50d648 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Deep Learning Model Security: Threats and Defenses Deep Learning using Rectified Linear Units (ReLU)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.655894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2054e25f-325e-4424-9bae-28e130ed2acf · outbound

This paper cites Stochastic gradient descent tricks.

Deep Learning Model Security: Threats and Defenses Stochastic gradient descent tricks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.663084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af731050-0fc1-4ed6-b2ff-4f4acf6fcf15 · outbound

This paper cites Deep learning and machine learning – object detection and semantic segmentation: From theory to applications.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning – object detection and semantic segmentation: From theory to applications

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.669130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d58233f-8a6c-4f0f-8096-1be7f94cf8b0 · outbound

This paper cites Deep learning and machine learning – python data structures and math- ematics fundamental: From theory to practice.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning – python data structures and math- ematics fundamental: From theory to practice

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:24:27.075119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5101f9b6-03f0-457f-9987-5ac566aab3c4 · outbound

This paper cites Review on methods to fix number of hidden neurons in neural networks.

Deep Learning Model Security: Threats and Defenses Review on methods to fix number of hidden neurons in neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.119600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da368dad-d64b-4711-b329-7b3f8b115bdc · outbound

This paper cites The influence of the sigmoid function parameters on the speed of backpropagation learning.

Deep Learning Model Security: Threats and Defenses The influence of the sigmoid function parameters on the speed of backpropagation learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.099418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8e927df-5f0e-429e-8b06-058fe993ae78 · outbound

This paper cites Learning both weights and connections for efficient neural network.

Deep Learning Model Security: Threats and Defenses Learning both weights and connections for efficient neural network

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.696111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d6e27a4-4d0c-4daf-a160-33973d828ab4 · outbound

This paper cites Deep learning, machine learning – digital signal and image processing: From theory to application.

Deep Learning Model Security: Threats and Defenses Deep learning, machine learning – digital signal and image processing: From theory to application

Reference 16

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:24:26.976174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40729590-7943-4ca8-bb6c-e28df75700b3 · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

Deep Learning Model Security: Threats and Defenses Adversarial Attacks on Neural Network Policies

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.707696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34795f1a-f8ae-4346-9e3a-597130d2d469 · outbound

This paper cites Certified defenses for data poisoning attacks.

Deep Learning Model Security: Threats and Defenses Certified defenses for data poisoning attacks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.714213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8bf3862-79dc-42a7-89e6-0718f36bceec · outbound

This paper cites Ensemble machine learning models for the detection of energy theft.

Deep Learning Model Security: Threats and Defenses Ensemble machine learning models for the detection of energy theft

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.058144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d07015d5-3ac9-457d-9db8-40e5cc37199c · outbound

This paper cites Fast Gradient Non-sign Methods.

Deep Learning Model Security: Threats and Defenses Fast Gradient Non-sign Methods

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.726429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f7920c6-25e9-42fe-81ca-0323617b2557 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

Deep Learning Model Security: Threats and Defenses Model inversion attacks that exploit confidence information and basic countermeasures

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.732079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e7735c7-e75d-4765-bdaf-3b6ac0a2d61c · outbound

This paper cites A survey on data poisoning attacks and defenses.

Deep Learning Model Security: Threats and Defenses A survey on data poisoning attacks and defenses

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:28.018405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1fb823dc-06d6-4f2f-9406-91ee58867cf4 · outbound

This paper cites an unresolved cited work.

Deep Learning Model Security: Threats and Defenses Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:24:27.999235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56b5e1b4-cdf9-4e57-b58f-866865ff14ba · outbound

This paper cites Securing large language models: Addressing bias, misinformation, and prompt attacks.

Deep Learning Model Security: Threats and Defenses Securing large language models: Addressing bias, misinformation, and prompt attacks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.748628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5bbd3c-1c39-43fb-bdfe-219525c50bc7 · outbound

This paper cites Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.753855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47d77c40-28eb-4768-bab9-8339793332c3 · outbound

This paper cites Deep learning and machine learning, advancing big data analytics and management: Unveiling BIBLIOGRAPHY 173 ai’s potential through tools, techniques, and applications.

Deep Learning Model Security: Threats and Defenses Deep learning and machine learning, advancing big data analytics and management: Unveiling BIBLIOGRAPHY 173 ai’s potential through tools, techniques, and applications

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:24:26.676831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c082e244-37dd-40e3-bfb2-14770b0df89e · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Deep Learning Model Security: Threats and Defenses Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.767968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb098449-3e97-45b9-871f-e7240e3bd25f · outbound

This paper cites Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021.

Deep Learning Model Security: Threats and Defenses Deep learning based vulnerability detection: Are we there yet? IEEE Transactions on Software Engineering , 48(9):3280–3296, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.980933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e24e5e2-395f-47ae-8bf0-0570423a637d · outbound

This paper cites An empirical study of deep learning models for vulnerability detection.

Deep Learning Model Security: Threats and Defenses An empirical study of deep learning models for vulnerability detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.961796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c49f5ee-7a92-48d2-b678-ceca6f9996d5 · outbound

This paper cites Shallow or deep? an empirical study on detecting vulnerabilities using deep learning.

Deep Learning Model Security: Threats and Defenses Shallow or deep? an empirical study on detecting vulnerabilities using deep learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.939650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3bf2ef94-5d59-458d-8c2b-4ac69254c585 · outbound

This paper cites Adversarial Manipulation of Deep Representations.

Deep Learning Model Security: Threats and Defenses Adversarial Manipulation of Deep Representations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.789387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 829ba41f-ad99-4a8a-a82c-371ea706903e · outbound

This paper cites A survey of the implementations of model inversion attacks.

Deep Learning Model Security: Threats and Defenses A survey of the implementations of model inversion attacks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.903631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c78ac99-ba87-4048-80ab-92a56dfac646 · outbound

This paper cites Practical black-box attacks against machine learning.

Deep Learning Model Security: Threats and Defenses Practical black-box attacks against machine learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.884788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ce5e6294-e606-48f2-9d67-9ccf55bd8853 · outbound

This paper cites Survey on white-box attacks and solutions.

Deep Learning Model Security: Threats and Defenses Survey on white-box attacks and solutions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.865655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c512e49d-30f5-4251-8c49-9e899f8e0696 · outbound

This paper cites Query efficient black-box adversarial attack on deep neural networks.

Deep Learning Model Security: Threats and Defenses Query efficient black-box adversarial attack on deep neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.847768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d5b4400-f48c-4632-99f3-d233dafae769 · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Deep Learning Model Security: Threats and Defenses Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.813902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9b746f6-55cc-48bf-a5d3-323fe8bbe083 · outbound

This paper cites Logit Pairing Methods Can Fool Gradient-Based Attacks.

Deep Learning Model Security: Threats and Defenses Logit Pairing Methods Can Fool Gradient-Based Attacks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.819455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8d1966-f219-4197-bfb5-236df05b4288 · outbound

This paper cites Adversarial attacks and defenses against deep neural networks: a survey.

Deep Learning Model Security: Threats and Defenses Adversarial attacks and defenses against deep neural networks: a survey

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.830683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3442380-250e-497f-bde1-0f7589de9176 · outbound

This paper cites Adversarial Examples that Fool Detectors.

Deep Learning Model Security: Threats and Defenses Adversarial Examples that Fool Detectors

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.831734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d5275fc-2749-41ab-b98f-3620cf9a68b3 · outbound

This paper cites Hands-on machine learning on google cloud platform: Implementing smart and efficient analytics using cloud ml engine.

Deep Learning Model Security: Threats and Defenses Hands-on machine learning on google cloud platform: Implementing smart and efficient analytics using cloud ml engine

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.814292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation decd9ee1-4cb0-4253-9c90-b7b51d303767 · outbound

This paper cites Gpt understands, too.

Deep Learning Model Security: Threats and Defenses Gpt understands, too

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.797601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2c6f6955-0838-483c-ad5c-99b0158c1963 · outbound

This paper cites Recent advances in convolutional neural networks.

Deep Learning Model Security: Threats and Defenses Recent advances in convolutional neural networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.847801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa3d7271-a9ec-4dc5-88f2-3f640e50a5f3 · outbound

This paper cites A comprehensive survey on poisoning attacks and countermeasures in machine learning.

Deep Learning Model Security: Threats and Defenses A comprehensive survey on poisoning attacks and countermeasures in machine learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.856066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d983b998-7f96-45c2-9877-a67c0b7f0f5a · outbound

This paper cites Deep model poisoning attack on feder- ated learning.

Deep Learning Model Security: Threats and Defenses Deep model poisoning attack on feder- ated learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.746259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 723b2ee5-11e1-4995-8048-7428c54945a1 · outbound

This paper cites A huber loss minimization approach to byzantine ro- bust federated learning.

Deep Learning Model Security: Threats and Defenses A huber loss minimization approach to byzantine ro- bust federated learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.728845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b8ae63de-1f52-4151-8909-226395feb95e · outbound

This paper cites Rethinking label flipping attack: From sample masking to sample thresholding.

Deep Learning Model Security: Threats and Defenses Rethinking label flipping attack: From sample masking to sample thresholding

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.708579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3d18cd12-75e4-47b2-acad-6d3226df967e · outbound

This paper cites Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks.

Deep Learning Model Security: Threats and Defenses Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.690556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 735da356-e070-4d22-9e9e-046039d21ca8 · outbound

This paper cites Generative adversarial nets.

Deep Learning Model Security: Threats and Defenses Generative adversarial nets

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.672348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b0388668-921f-4775-ac7f-ab6fb1959976 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Deep Learning Model Security: Threats and Defenses Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.890469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df52c534-156b-4a35-973d-a40642f8cb64 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Deep Learning Model Security: Threats and Defenses Towards evaluating the robustness of neural networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.898738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a95f040f-6848-413d-9d3d-cfd8422a3a09 · outbound

This paper cites Reflections on trusting trust.

Deep Learning Model Security: Threats and Defenses Reflections on trusting trust

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.905020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55f7a838-fd33-48a9-a59d-cbfaa2abce7b · outbound

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

Deep Learning Model Security: Threats and Defenses BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.910669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cea5925-2e2a-417d-baff-ea89ae3cb428 · outbound

This paper cites An assessment by the statin intolerance panel: 2014 update.

Deep Learning Model Security: Threats and Defenses An assessment by the statin intolerance panel: 2014 update

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.627572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 339cadad-fd56-4ab0-9087-270393b811d8 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Deep Learning Model Security: Threats and Defenses Explaining and Harnessing Adversarial Examples

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.921555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 298a8811-3f3c-44c2-aeed-f8599ed2f346 · outbound

This paper cites The limitations of deep learning in adversarial settings.

Deep Learning Model Security: Threats and Defenses The limitations of deep learning in adversarial settings

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.607780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3ff2cddf-dabf-4f02-b9a3-8b7684fa710e · outbound

This paper cites Poisoning Attacks and Defenses on Artificial Intelligence: A Survey.

Deep Learning Model Security: Threats and Defenses Poisoning Attacks and Defenses on Artificial Intelligence: A Survey

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.932602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91d85854-0dec-4b64-87be-c9f58a9e2af5 · outbound

This paper cites Robust nonparametric regression under poisoning attack.

Deep Learning Model Security: Threats and Defenses Robust nonparametric regression under poisoning attack

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.587985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ef301e5-7126-4564-955f-4b03fa5264a8 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Deep Learning Model Security: Threats and Defenses Calibrating noise to sensitivity in private data analysis

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.567831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 244228f9-02ca-4983-a48b-5215bdf59db6 · outbound

This paper cites Robust federated learning with realistic corruption.

Deep Learning Model Security: Threats and Defenses Robust federated learning with realistic corruption

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.550701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3a1aaf55-3f09-427a-81f5-01b1cf835c20 · outbound

This paper cites High dimensional distributed gradient descent with arbitrary number of byzantine attackers.

Deep Learning Model Security: Threats and Defenses High dimensional distributed gradient descent with arbitrary number of byzantine attackers

Reference 60

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:24:26.403824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6c648534-42a6-4730-88bd-27fdc1d9a277 · outbound

This paper cites Protocols for secure computations.

Deep Learning Model Security: Threats and Defenses Protocols for secure computations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.531182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a12a609e-b84a-4c1d-8c79-7ea52a6333fe · outbound

This paper cites Privacy preserving generative adversarial networks to model electronic health records.

Deep Learning Model Security: Threats and Defenses Privacy preserving generative adversarial networks to model electronic health records

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.512705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3d83fb98-44e3-43e4-97fb-009775feac93 · outbound

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

Deep Learning Model Security: Threats and Defenses Neural cleanse: Identifying and mitigating backdoor attacks in neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.485819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9bcbfb0f-d7ee-4128-abf9-7b03d8e81820 · outbound

This paper cites A survey on contrastive self-supervised learning.

Deep Learning Model Security: Threats and Defenses A survey on contrastive self-supervised learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.979887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d57a120e-95f7-4cbc-a744-c282942175cb · outbound

This paper cites Unsupervised visual representation learning by context prediction.

Deep Learning Model Security: Threats and Defenses Unsupervised visual representation learning by context prediction

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.452417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 80110ee1-a6fd-46d0-9334-c6d553e7843d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Deep Learning Model Security: Threats and Defenses Distilling the Knowledge in a Neural Network

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:25.990309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c7dd140-dec0-479a-b1c1-0b0a6c8e37ae · outbound

This paper cites Adversarial attack on graph structured data.

Deep Learning Model Security: Threats and Defenses Adversarial attack on graph structured data

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.433395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ee40af29-d022-4f59-9177-2837cb838e88 · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

Deep Learning Model Security: Threats and Defenses Poisoning Attacks against Support Vector Machines

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.002330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24681ff7-bebb-46b8-8f3f-09f1589831ec · outbound

This paper cites Protecting sensitive knowledge by data sanitization.

Deep Learning Model Security: Threats and Defenses Protecting sensitive knowledge by data sanitization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.410342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d956e29d-6195-4e91-9c78-b27506d46d1f · outbound

This paper cites Defending Model Inversion and Membership Inference Attacks via Prediction Purification.

Deep Learning Model Security: Threats and Defenses Defending Model Inversion and Membership Inference Attacks via Prediction Purification

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.266624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b4f52ba4-f23f-4fe7-a7b0-7b820eb892be · outbound

This paper cites Differential privacy: A survey of results.

Deep Learning Model Security: Threats and Defenses Differential privacy: A survey of results

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.386931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f0a72494-5677-4126-aff1-60ada85f98df · outbound

This paper cites A Survey on Poisoning Attacks Against Supervised Machine Learning.

Deep Learning Model Security: Threats and Defenses A Survey on Poisoning Attacks Against Supervised Machine Learning

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.240530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dcddf8d9-66d6-4e79-ade9-57c161e88c92 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

Deep Learning Model Security: Threats and Defenses Robust loss functions under label noise for deep neural networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.366483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34edffb4-e149-4ae1-b88b-2c451325620a · outbound

This paper cites Online anomaly detection under adversarial impact.

Deep Learning Model Security: Threats and Defenses Online anomaly detection under adversarial impact

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.347657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1c1a70fd-37db-4f0f-ab55-3bc9dd0c6b8d · outbound

This paper cites Face recognition systems: A survey.

Deep Learning Model Security: Threats and Defenses Face recognition systems: A survey

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.326866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 120b946b-1aab-4b62-b870-cb14fbd931c2 · outbound

This paper cites A comprehensive review on malware detection approaches.

Deep Learning Model Security: Threats and Defenses A comprehensive review on malware detection approaches

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.301620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6bd75f38-be18-4321-b60f-7f694e97e63a · outbound

This paper cites A Review of Network Traffic Analysis and Prediction Techniques.

Deep Learning Model Security: Threats and Defenses A Review of Network Traffic Analysis and Prediction Techniques

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:24:26.209585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b16b4d08-4530-45ac-bb4d-bc48b7f687fc · outbound

This paper cites Automated Cyber Defence: A Review.

Deep Learning Model Security: Threats and Defenses Automated Cyber Defence: A Review

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.067370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029b2624-a53a-4e95-b357-bd05a60586b0 · outbound

This paper cites No more chewy centers: Introducing the zero trust model of information security.

Deep Learning Model Security: Threats and Defenses No more chewy centers: Introducing the zero trust model of information security

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.281490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40edd535-7ded-4435-92c0-c09acb80e921 · outbound

This paper cites Generative adversarial networks.

Deep Learning Model Security: Threats and Defenses Generative adversarial networks

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.263828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ddc34542-0057-49a9-a69f-c74516438079 · outbound

This paper cites Dynamical Variational Autoencoders: A Comprehensive Review.

Deep Learning Model Security: Threats and Defenses Dynamical Variational Autoencoders: A Comprehensive Review

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T17:24:26.086247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 474b858b-ed3d-46b6-9589-6da31eb329da · outbound

This paper cites Secure multi-party computation: theory, practice and applications.

Deep Learning Model Security: Threats and Defenses Secure multi-party computation: theory, practice and applications

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.242052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69e21d91-4148-44e8-81de-5d9417a0ec29 · outbound

This paper cites Homomorphic encryption.

Deep Learning Model Security: Threats and Defenses Homomorphic encryption

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.223530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation adca9c7f-9bbc-41eb-96db-cea4df5a4016 · outbound

This paper cites A review of applications in federated learning.

Deep Learning Model Security: Threats and Defenses A review of applications in federated learning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:24:27.205307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation f1a692df-d60d-413f-8506-cd31bd64a80b · inbound

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions cites this paper.

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions Deep Learning Model Security: Threats and Defenses

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:54:34.403192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 621cf58c-e0d6-42ea-81c3-d49222504b18 · inbound

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift cites this paper.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift Deep Learning Model Security: Threats and Defenses

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.