Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices | Paper Citation Record

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This paper cites Under- standing robustness of transformers for image classification.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Under- standing robustness of transformers for image classification

Reference 1

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This paper cites End-to- end object detection with transformers.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices End-to- end object detection with transformers

Reference 2

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Observation cf50f527-1eb3-42bd-a171-e6cb331082e7 · outbound

This paper cites Overload: Latency attacks on object detection for edge devices.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Overload: Latency attacks on object detection for edge devices

Reference 3

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This paper cites Nmtsloth: understanding and testing efficiency degra- dation of neural machine translation systems.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Nmtsloth: understanding and testing efficiency degra- dation of neural machine translation systems

Reference 4

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This paper cites Dif- fusiondet: Diffusion model for object detection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Dif- fusiondet: Diffusion model for object detection

Reference 5

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Observation 392f7c3e-ce03-40fd-a57f-3db955320276 · outbound

This paper cites Adversarial objectness gradient attacks in real- time object detection systems.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Adversarial objectness gradient attacks in real- time object detection systems

Reference 6

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This paper cites Dynamic detr: End-to-end object detection with dynamic attention.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Dynamic detr: End-to-end object detection with dynamic attention

Reference 7

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Observation 77c559f1-2704-4b00-afee-62c63e66e695 · outbound

This paper cites The pascal visual object classes challenge: A retrospective.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices The pascal visual object classes challenge: A retrospective

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.519238Z

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Observation 8519edc4-34f5-4843-9e45-e609c3be0f25 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 9

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This paper cites Ilfo: Adversarial attack on adaptive neural networks.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Ilfo: Adversarial attack on adaptive neural networks

Reference 10

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Observation d7e9555e-38f2-414f-8b5b-9100427cbb44 · outbound

This paper cites α-iou: A family of power intersection over union losses for bounding box regression.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices α-iou: A family of power intersection over union losses for bounding box regression

Reference 11

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Observation 29218dc6-ce53-4cc3-a232-d6d257ac4c35 · outbound

This paper cites Learning non-maximum suppression.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Learning non-maximum suppression

Reference 12

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Observation 6abde0ae-589f-4b42-85ed-1721005a8c7f · outbound

This paper cites Adversarial attack and defense of yolo detectors in autonomous driving scenarios.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Adversarial attack and defense of yolo detectors in autonomous driving scenarios

Reference 13

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This paper cites Distill- ing robust and non-robust features in adversarial examples by information bottleneck.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Distill- ing robust and non-robust features in adversarial examples by information bottleneck

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.443899Z

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This paper cites Robust Adversarial Perturbation on Deep Proposal-based Models.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Robust Adversarial Perturbation on Deep Proposal-based Models

Reference 15

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Observation 7365cc7f-50a4-40b7-8303-e5545be39f68 · outbound

This paper cites Aide: An automatic data engine for object detection in autonomous driving.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Aide: An automatic data engine for object detection in autonomous driving

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.432023Z

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Observation df42c267-b39d-4147-b593-efd45a039d0a · outbound

This paper cites Microsoft coco: Common objects in context.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Microsoft coco: Common objects in context

Reference 17

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This paper cites Ssd: Single shot multibox detector.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Ssd: Single shot multibox detector

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.413611Z

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Observation a80e3df1-2f85-4728-abf4-dd66de35fd51 · outbound

This paper cites Decoupled Weight Decay Regularization.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Decoupled Weight Decay Regularization

Reference 19

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This paper cites Multi-View Do- main Adaptive Object Detection in Surveillance Cameras.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Multi-View Do- main Adaptive Object Detection in Surveillance Cameras

Reference 20

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Observation acf3677c-9ca1-46e2-8630-4d2d724dbc52 · outbound

This paper cites Slowtrack: Increasing the latency of camera-based percep- tion in autonomous driving using adversarial examples.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Slowtrack: Increasing the latency of camera-based percep- tion in autonomous driving using adversarial examples

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.389425Z

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Observation cdb03b36-fb05-484d-9b44-1142c1158c0f · outbound

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

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 22

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Observation d9896742-d820-48eb-a562-3a4632a738ab · outbound

This paper cites Efficient non- maximum suppression.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Efficient non- maximum suppression

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.377940Z

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Observation 7b3c4192-d180-4750-ba7f-ddc8462bd8e2 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices YOLOv3: An Incremental Improvement

Reference 24

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This paper cites You only look once: Unified, real-time object de- tection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices You only look once: Unified, real-time object de- tection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:00.921146Z

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Observation 8958345f-8328-4df6-b835-d376b3f363f5 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.360275Z

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Observation 2dab5d6c-d13b-4f24-941a-5bd3cd5ec363 · outbound

This paper cites Generalized in- tersection over union: A metric and a loss for bounding box regression.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Generalized in- tersection over union: A metric and a loss for bounding box regression

Reference 27

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Observation 495ee491-5a49-4358-b4d1-8377da92c4a4 · outbound

This paper cites Phantom sponges: Exploiting non- maximum suppression to attack deep object detectors.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Phantom sponges: Exploiting non- maximum suppression to attack deep object detectors

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.342885Z

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Observation ce8c02dd-9450-44d2-bc85-c6a6f6b7a7df · outbound

This paper cites Flexible High-resolution Object Detection on Edge Devices with Tunable Latency.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Flexible High-resolution Object Detection on Edge Devices with Tunable Latency

Reference 29

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Observation 3ae57311-5112-4af2-82a7-bce591b155f1 · outbound

This paper cites Sponge examples: Energy-latency attacks on neural networks.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Sponge examples: Energy-latency attacks on neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.320960Z

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This paper cites Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism

Reference 31

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Observation c8fbf7b6-4e02-4cda-9acf-3c6be39fe6b0 · outbound

This paper cites default hyp in yolo.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices default hyp in yolo

Reference 32

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Observation f58dea8e-c36b-4854-91cd-4f080ee363a1 · outbound

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Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Unresolved cited work

Reference 33

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Observation ba66768d-50d5-4ea7-9322-561fb4a5b952 · outbound

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Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Unresolved cited work

Reference 34

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Observation d47c977f-6e12-42a6-bce1-e643a782b6a6 · outbound

This paper cites Daedalus: Breaking nonmaximum suppression in object detection via adversarial examples.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Daedalus: Breaking nonmaximum suppression in object detection via adversarial examples

Reference 35

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Observation 28a82b6f-295e-40e0-ab4d-c66fa20d7726 · outbound

This paper cites Generalized uav object detec- tion via frequency domain disentanglement.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Generalized uav object detec- tion via frequency domain disentanglement

Reference 36

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Observation d54fb406-02cf-4737-9324-0c7d1cbf1425 · outbound

This paper cites Balance, imbalance, and rebalance: Under- standing robust overfitting from a minimax game perspec- tive.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Balance, imbalance, and rebalance: Under- standing robust overfitting from a minimax game perspec- tive

Reference 37

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Observation c936a3a9-5ff7-42d8-80fa-d0f6961c44bd · outbound

This paper cites Adversar- ial weight perturbation helps robust generalization.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Adversar- ial weight perturbation helps robust generalization

Reference 38

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Observation 6e09afad-0124-470d-845c-4ef49a20fbaf · outbound

This paper cites You see what i want you to see: Exploring targeted black-box transferability attack for hash-based image retrieval systems.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices You see what i want you to see: Exploring targeted black-box transferability attack for hash-based image retrieval systems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.219782Z

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Observation 0c86f3ad-3526-4161-821f-0a3a729edc8a · outbound

This paper cites Adversarial examples for se- mantic segmentation and object detection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Adversarial examples for se- mantic segmentation and object detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.207719Z

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Observation 95ad2497-87de-4fc2-b0ab-7d0a2ab7a1ba · outbound

This paper cites Adc: Adversarial attacks against object detection that evade context consistency checks.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Adc: Adversarial attacks against object detection that evade context consistency checks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.194713Z

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Observation 8360dcab-e91c-4b96-9f57-ee6698778f64 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 42

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This paper cites Towards adversarially ro- bust object detection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Towards adversarially ro- bust object detection

Reference 43

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Observation b25114f9-3ae5-4c19-ae59-ff9f72d5c724 · outbound

This paper cites Xing, Laurent El Ghaoui, and Michael I.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Xing, Laurent El Ghaoui, and Michael I

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.164666Z

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Observation 9acfdec3-e168-43ca-96d4-6e14183a3c7f · outbound

This paper cites Dynamic r-cnn: Towards high quality object detection via dynamic training.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Dynamic r-cnn: Towards high quality object detection via dynamic training

Reference 45

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This paper cites Detrs beat yolos on real-time object detection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Detrs beat yolos on real-time object detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.146315Z

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Observation 31a2ff70-7696-4c64-9f3f-2257a4f41f64 · outbound

This paper cites Enhancing ge- ometric factors in model learning and inference for object detection and instance segmentation.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Enhancing ge- ometric factors in model learning and inference for object detection and instance segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.134273Z

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Observation 543ffed9-c66c-426c-8b4f-73cc3e72fb1c · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 48

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Observation 34680ca5-8246-4da4-af84-a5a7ca630c01 · outbound

This paper cites Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023

Reference 49

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Observation 47fff184-c918-4374-88b9-80438614785a · outbound

This paper cites 1, 2, 4, 6.

Can't Slow me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices 1, 2, 4, 6

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:51:01.287580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers