AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain | Paper Citation Record

Observation 7845d925-e372-4b5f-8d77-10a076112a1b · outbound

This paper cites Yolo-g: A lightweight network model for improving the performance of military targets detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Yolo-g: A lightweight network model for improving the performance of military targets detection,

Reference 1

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6f1d123c-2cc6-433a-af19-4aa67c4a697d · outbound

This paper cites Deep learning for automatic target recognition with real and synthetic infrared maritime imagery,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Deep learning for automatic target recognition with real and synthetic infrared maritime imagery,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:23.030964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d1666eb-6f97-4c9b-9987-9d032feb22b2 · outbound

This paper cites Improving object detector training on synthetic data by starting with a strong baseline methodology,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Improving object detector training on synthetic data by starting with a strong baseline methodology,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:23.020487Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8c76536-a9ae-4968-84f1-12c86de62a19 · outbound

This paper cites Transforming the multidomain battlefield with ai,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Transforming the multidomain battlefield with ai,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:23.006752Z

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Observation 033254a1-421f-4e62-9842-8c92a5d68cf9 · outbound

This paper cites A system-driven taxonomy of attacks and defenses in adversarial machine learning,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain A system-driven taxonomy of attacks and defenses in adversarial machine learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.994911Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95ac2f93-421f-4dcf-b718-554237c52f80 · outbound

This paper cites Adversarial machine learning: A taxonomy and ter- minology of attacks and mitigations,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Adversarial machine learning: A taxonomy and ter- minology of attacks and mitigations,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.984231Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b7c5199-6f22-4951-9b47-e969123292e3 · outbound

This paper cites Adversarial ai in the cyber domain,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Adversarial ai in the cyber domain,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.973541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b09943d4-880e-4324-b56c-4d29a45171be · outbound

This paper cites Baddet: Backdoor attacks on object detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Baddet: Backdoor attacks on object detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.962656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d8e4c26-7233-4e28-92d3-b09b375e25f2 · outbound

This paper cites Reflection backdoor: A natural backdoor attack on deep neural networks,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Reflection backdoor: A natural backdoor attack on deep neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.952954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fbf6483c-ad13-444b-961d-583199cae96c · outbound

This paper cites Narcissus: A practical clean-label backdoor attack with limited information,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Narcissus: A practical clean-label backdoor attack with limited information,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.942587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0c069e41-f1a6-4f03-93ee-422d9436cb8c · outbound

This paper cites Defending against adversarial ai attacks: an overview,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Defending against adversarial ai attacks: an overview,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.932622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0bb13318-900a-4ba4-85b4-88391b4fed00 · outbound

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

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Neural polarizer: A lightweight and effective backdoor defense via purifying poisoned features,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.922320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1583b230-e95c-4a92-ac10-c39cf32723ff · outbound

This paper cites Towards a proactive {ML} approach for detecting backdoor poison samples,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Towards a proactive {ML} approach for detecting backdoor poison samples,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.911100Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 64055112-ad76-4063-9994-02b390b54ccf · outbound

This paper cites Pad: Patch-agnostic defense against adversarial patch attacks,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Pad: Patch-agnostic defense against adversarial patch attacks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.900833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b5d8d21-79a7-4c06-83d1-67324d334ca8 · outbound

This paper cites Efficientad: Accurate visual anomaly detection at millisecond- level latencies,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Efficientad: Accurate visual anomaly detection at millisecond- level latencies,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.890420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ac69b31-a287-4a14-82f3-6552cd2db96c · outbound

This paper cites The mvtec ad 2 dataset: Ad- vanced scenarios for unsupervised anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain The mvtec ad 2 dataset: Ad- vanced scenarios for unsupervised anomaly detection,

Reference 16

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Observation 5b950a4a-e01f-4603-8c11-dde329cf632d · outbound

This paper cites Towards total recall in in- dustrial anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Towards total recall in in- dustrial anomaly detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.880940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 528da229-7b06-4306-b38d-53c25bc604bc · outbound

This paper cites Threats to training: A survey of poisoning attacks and defenses on machine learning systems,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Threats to training: A survey of poisoning attacks and defenses on machine learning systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.870400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7beceed-2b7c-44c9-8313-e3ee3de8196f · outbound

This paper cites Bullseye polytope: A scalable clean- label poisoning attack with improved transferability,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Bullseye polytope: A scalable clean- label poisoning attack with improved transferability,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.859617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c0e7821f-e429-455a-bd3d-d693422bbfce · outbound

This paper cites Color backdoor: A robust poisoning attack in color space,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Color backdoor: A robust poisoning attack in color space,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.848256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85332897-df04-4b33-bb29-dfd18924fd79 · outbound

This paper cites Clean-image backdoor: Attacking multi-label models with poisoned labels only,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Clean-image backdoor: Attacking multi-label models with poisoned labels only,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.838132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35d05134-bafc-4eeb-84d2-c4e49379acea · outbound

This paper cites A dilution-based defense method against poisoning attacks on deep learning systems,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain A dilution-based defense method against poisoning attacks on deep learning systems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.828564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01516acd-e2c2-4eb4-b8d4-c18813d04a5d · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.818499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ad2b7bd6-322b-45fd-b749-0f570140cca8 · outbound

This paper cites Towards stable backdoor purification through feature shift tuning,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Towards stable backdoor purification through feature shift tuning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.808605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e98910b9-3a02-451b-870c-8369605c0ba7 · outbound

This paper cites Black-box backdoor defense via zero-shot image purification,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Black-box backdoor defense via zero-shot image purification,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.798169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14253963-2889-45aa-ac33-2ba49ac7a433 · outbound

This paper cites Napguard: Towards detecting naturalistic adversarial patches,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Napguard: Towards detecting naturalistic adversarial patches,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.788174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c62357c-3153-48bd-ae07-ee1836ef18d0 · outbound

This paper cites Detectorguard: Provably securing object detectors against localized patch hiding attacks,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Detectorguard: Provably securing object detectors against localized patch hiding attacks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.777493Z

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Observation 90efdcce-4c7d-4dbb-bb11-7ff38cfba594 · outbound

This paper cites Test-time backdoor detection for object detection models,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Test-time backdoor detection for object detection models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.767272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 71b1ef35-4a82-4dd8-9105-378a960492cb · outbound

This paper cites Local gradients smoothing: Defense against localized adversarial attacks,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Local gradients smoothing: Defense against localized adversarial attacks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.756773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30700bb1-bc4c-449a-96f7-90e9f68305bb · outbound

This paper cites Segment and complete: Defending object detectors against adversarial patch attacks with robust patch detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Segment and complete: Defending object detectors against adversarial patch attacks with robust patch detection,

Reference 30

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51a74471-d65f-4ca4-b59d-3b9d464c5a4e · outbound

This paper cites Jedi: Entropy-based localization and removal of adversarial patches,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Jedi: Entropy-based localization and removal of adversarial patches,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.734589Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72b6f8c0-d8b5-4d54-809d-dcbc2cfb3186 · outbound

This paper cites A unified, resilient, and explainable adversarial patch detector,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain A unified, resilient, and explainable adversarial patch detector,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.723520Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 93f599f7-12ab-4e5b-8f89-0d52cf947be7 · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Natural synthetic anomalies for self-supervised anomaly detection and localization,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.711997Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f18665ea-c80a-4b2f-8559-4fe9ae46a4a9 · outbound

This paper cites PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,

Reference 34

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Observation ef9647e7-e623-4e73-9ee5-742cd4d3d457 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.691908Z

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Observation a0405351-b3da-4768-918e-91118312d4bb · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.682161Z

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Observation 7d2bd002-9715-4447-94b0-d0749bcd3363 · outbound

This paper cites Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.672546Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7049db35-2978-413f-b08e-4f0563259d5b · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 38

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Observation 4c22d463-55a7-45f1-8f12-81ce350f405a · outbound

This paper cites A survey on unsupervised anomaly detection algorithms for industrial images,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain A survey on unsupervised anomaly detection algorithms for industrial images,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.662657Z

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Observation 30fa6889-c65a-4e39-ab73-e6a73079faa1 · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.652820Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d17e8cb-a443-4948-a342-c738009df4ff · outbound

This paper cites Anomaly detection in nanofibrous materials by cnn-based self-similarity,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Anomaly detection in nanofibrous materials by cnn-based self-similarity,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.642719Z

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Observation f37a1f5c-a7d4-4af1-a800-9a81b2584e17 · outbound

This paper cites Unsupervised surface anomaly detection with diffusion probabilistic model,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Unsupervised surface anomaly detection with diffusion probabilistic model,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.633193Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 54ab2c74-208b-4689-93fe-1918a96876e6 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 43

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Observation 6e9853e4-fd23-4163-81be-f45f5a3fb20a · outbound

This paper cites Modeling the distribution of normal data in pre-trained deep features for anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Modeling the distribution of normal data in pre-trained deep features for anomaly detection,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.623680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ca2a349-d413-4645-bd84-c04bfe10016c · outbound

This paper cites Unsupervised anomaly localization using variational auto-encoders,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Unsupervised anomaly localization using variational auto-encoders,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.613976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c253011c-d2c3-4005-aa0c-51185d271e88 · outbound

This paper cites Gan-based anomaly detection: A review,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Gan-based anomaly detection: A review,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.604607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bc83c2f9-5b5d-4eb0-ab6c-689f39899c17 · outbound

This paper cites An unsupervised generative adversarial network-based method for defect inspection of texture surfaces,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain An unsupervised generative adversarial network-based method for defect inspection of texture surfaces,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.594745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe610834-6ae0-4927-93ab-8d531bc71a30 · outbound

This paper cites Visual detection of generic defects in industrial components using generative adversarial networks,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Visual detection of generic defects in industrial components using generative adversarial networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.585682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2e78670-1555-47d4-84af-6abe4fba5cf1 · outbound

This paper cites Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.576177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bcb71d93-d510-43ca-899b-9ff27c52c8c6 · outbound

This paper cites Uninet: A contrastive learning-guided unified framework with feature selection for anomaly detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Uninet: A contrastive learning-guided unified framework with feature selection for anomaly detection,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.566529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5292293b-03dd-4ca9-898d-b8fe67e6520b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Adam: A Method for Stochastic Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T11:10:20.819696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b9233d8-e0d7-4c6c-ada3-7d0edf3c3fae · outbound

This paper cites The pascal visual object classes (voc) challenge,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain The pascal visual object classes (voc) challenge,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.555750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 595fcf19-c6c8-49ed-834d-0ffa3b4be62d · outbound

This paper cites Microsoft coco: Common objects in context,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Microsoft coco: Common objects in context,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.542797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdd9bfa7-22c8-43ff-a45f-c143460913bc · outbound

This paper cites Open-source datasets for image processing and artificial intelligence research: A compar- ison of imagenet and ms coco datasets,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Open-source datasets for image processing and artificial intelligence research: A compar- ison of imagenet and ms coco datasets,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.531381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ff41c2ef-07f7-423f-a675-4ee138aada4c · outbound

This paper cites Milcivveh computer vision dataset,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Milcivveh computer vision dataset,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.519742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2e59820-b7c2-495c-8add-470098633300 · outbound

This paper cites Segment anything,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Segment anything,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.509122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 30fde22d-7c14-459c-8a19-7f390f50b924 · outbound

This paper cites {Meta-Sift}: How to sift out a clean subset in the presence of data poisoning?,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain {Meta-Sift}: How to sift out a clean subset in the presence of data poisoning?,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.498222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e433eed5-57f2-447c-87f6-5810ba584a06 · outbound

This paper cites Apricot: A dataset of physical adversarial attacks on object detection,.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Apricot: A dataset of physical adversarial attacks on object detection,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:10:22.472873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers