Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors | Paper Citation Record

Observation 19c6ea73-8dfd-471d-b722-0523a8e251b4 · outbound

This paper cites Traffic light mapping and detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic light mapping and detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.508846Z

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.

Observation 85915943-8337-4878-92b6-d57655d64fbc · outbound

This paper cites Traffic light recognition using deep learning and prior maps for autonomous cars,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic light recognition using deep learning and prior maps for autonomous cars,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.499833Z

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.

Observation 87bcd682-30e2-4cb0-89ed-31d39c6fa593 · outbound

This paper cites Intriguing Properties of Neural Networks,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Intriguing Properties of Neural Networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.489977Z

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.

Observation 5f45c8b1-9d65-4930-b878-286d998d51a6 · outbound

This paper cites Explaining and Harness- ing Adversarial Examples,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Explaining and Harness- ing Adversarial Examples,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.480380Z

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.

Observation 1f2d426d-8ec6-4076-87e6-5ac0e183da3e · outbound

This paper cites Adversarial Patch,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Adversarial Patch,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.470676Z

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.

Observation a2021e9f-a8ae-457c-8367-ff4ba6881bec · outbound

This paper cites Evalu- ating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Evalu- ating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.461332Z

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.

Observation 205096b7-0ad3-459f-9827-621ecab2034e · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Robust physical-world attacks on deep learning visual classification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.451970Z

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.

Observation c9068850-d865-40c5-a0b0-ebcc4a73386a · outbound

This paper cites Adversarial sticker: A stealthy attack method in the physical world,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Adversarial sticker: A stealthy attack method in the physical world,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.443124Z

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.

Observation d5781805-0694-4291-b896-5e88044270ac · outbound

This paper cites Feasibility and sup- pression of adversarial patch attacks on end-to-end vehicle control,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Feasibility and sup- pression of adversarial patch attacks on end-to-end vehicle control,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.433449Z

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.

Observation 3013aa1c-8191-4cdd-9ff3-ed34ae88df6a · outbound

This paper cites Effects of and defenses against adversarial attacks on a traffic light classification cnn,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Effects of and defenses against adversarial attacks on a traffic light classification cnn,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.424833Z

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.

Observation 728c023e-f1c9-4b03-a544-f7f6fcb8df6a · outbound

This paper cites SITAR: evaluating the adversarial robustness of traffic light recognition in level-4 autonomous driving,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors SITAR: evaluating the adversarial robustness of traffic light recognition in level-4 autonomous driving,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.415879Z

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.

Observation e56b75f9-ab3a-4463-a0c3-5d1c6f00f6f7 · outbound

This paper cites Rolling colors: Adversarial laser exploits against traffic light recognition,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Rolling colors: Adversarial laser exploits against traffic light recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.406669Z

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.

Observation 035514f2-0521-4130-83c3-f474d71d4b5e · outbound

This paper cites On the vulnerability of traffic light recognition systems to laser illumination attacks,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors On the vulnerability of traffic light recognition systems to laser illumination attacks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.397130Z

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.

Observation aedfcf2b-9e5b-420d-a467-3f0acb2315a6 · outbound

This paper cites Deepbillboard: Systematic physical-world testing of autonomous driving systems,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Deepbillboard: Systematic physical-world testing of autonomous driving systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.387533Z

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.

Observation 73b2598a-3d9c-4e72-999c-11b94612d1bb · outbound

This paper cites TLD-READY: traffic light detection - relevance estimation and deployment analysis,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors TLD-READY: traffic light detection - relevance estimation and deployment analysis,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.377223Z

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.

Observation a4d56762-daa4-4c28-bd86-9b8f8e92c18f · outbound

This paper cites From door to door—principles and applications of computer vision for driver assistant systems,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors From door to door—principles and applications of computer vision for driver assistant systems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.367827Z

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.

Observation 429c28b5-f86b-4ab2-99a1-107b5db70c41 · outbound

This paper cites A vision-based traffic light detection system at intersections,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors A vision-based traffic light detection system at intersections,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.358654Z

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.

Observation a6e844dd-251e-4454-ae41-f219c9356fb4 · outbound

This paper cites Robust recognition of traffic signals,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Robust recognition of traffic signals,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.349950Z

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.

Observation ff040fdf-695e-4ea9-b8f2-58b838ea57ab · outbound

This paper cites Visual state estima- tion of traffic lights using hidden markov models,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Visual state estima- tion of traffic lights using hidden markov models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.341308Z

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.

Observation ba8b17ff-ca59-45e3-bb4b-0c322a6567fd · outbound

This paper cites Traffic light recognition using convolutional neural networks: A survey,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic light recognition using convolutional neural networks: A survey,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.332750Z

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.

Observation 34268bd4-c9e4-4d0b-a944-f51c562cf255 · outbound

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

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors You only look once: Unified, real-time object detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.324315Z

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.

Observation b19ca7db-145b-494e-ba08-920591199833 · outbound

This paper cites Ssd: Single shot multibox detector,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Ssd: Single shot multibox detector,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.315359Z

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.

Observation 9049a220-6853-4977-96a3-57b06fbea182 · outbound

This paper cites Vision for looking at traffic lights: Issues, survey, and perspectives,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Vision for looking at traffic lights: Issues, survey, and perspectives,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.306606Z

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.

Observation ec9fa99f-e3e1-46b2-afbf-2e180013bda0 · outbound

This paper cites Detecting traffic lights by single shot detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Detecting traffic lights by single shot detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.297808Z

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.

Observation a3220457-26b9-4ac8-9032-3957f27bdd66 · outbound

This paper cites A hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors A hierarchical deep architecture and mini-batch selection method for joint traffic sign and light detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.288901Z

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.

Observation d0f5653f-9760-4bf2-8f49-544882aac162 · outbound

This paper cites Deep convolutional traffic light recognition for automated driving,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Deep convolutional traffic light recognition for automated driving,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.280253Z

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.

Observation fe458551-5e0a-4a53-99c1-07dc3bdbc4cf · outbound

This paper cites Real-time traffic light detection and recognition based on deep retinanet for self driving cars,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Real-time traffic light detection and recognition based on deep retinanet for self driving cars,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.271641Z

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.

Observation 0821f706-1d37-444f-857b-4fe62346b8dc · outbound

This paper cites A comparative study between state-of-the-art object detectors for traffic light detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors A comparative study between state-of-the-art object detectors for traffic light detection,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.263043Z

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.

Observation 0b2ad625-a0b0-4e5f-889b-92ccd07e73d5 · outbound

This paper cites An end-to-end traffic light detection algorithm based on deep learning,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors An end-to-end traffic light detection algorithm based on deep learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.254380Z

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.

Observation 31f9fe89-a20c-41a3-bd75-53edceec4f3f · outbound

This paper cites Traffic light detection based on depth improved yolov5,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic light detection based on depth improved yolov5,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.245130Z

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.

Observation a7197b45-63c3-434e-9758-e74056159680 · outbound

This paper cites Real-time small traffic sign detection with revised faster-rcnn,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Real-time small traffic sign detection with revised faster-rcnn,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.236032Z

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.

Observation 6ffdcd3a-e6bb-44dd-90eb-0ed05f6e7ed0 · outbound

This paper cites Traffic lights detection and recognition method based on the improved yolov4 algorithm,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic lights detection and recognition method based on the improved yolov4 algorithm,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.226937Z

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.

Observation c2a885ae-e544-4e37-935e-ccba31260621 · outbound

This paper cites Fast traffic sign and light detection using deep learning for automotive applications,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Fast traffic sign and light detection using deep learning for automotive applications,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.217656Z

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.

Observation 89064089-d066-40b4-ab00-f29f37124288 · outbound

This paper cites Deeptlr: A single deep convolutional network for detection and classification of traffic lights,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Deeptlr: A single deep convolutional network for detection and classification of traffic lights,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.207802Z

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.

Observation 56e088c4-6c7b-49dc-a38a-4f87e2c0484c · outbound

This paper cites Hdtlr: A cnn based hierarchical detector for traffic lights,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Hdtlr: A cnn based hierarchical detector for traffic lights,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.198420Z

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.

Observation ded1eb49-d3ac-4081-861f-a4cbe7f482f9 · outbound

This paper cites Traffic light recog- nition in varying illumination using deep learning and saliency map,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Traffic light recog- nition in varying illumination using deep learning and saliency map,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.189121Z

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.

Observation d6360d40-c8a2-403a-8d6e-d5c7e6ef5624 · outbound

This paper cites Saliency map generation by the convolutional neural network for real-time traffic light detection using template matching,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Saliency map generation by the convolutional neural network for real-time traffic light detection using template matching,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.179690Z

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.

Observation 79ec9c8b-e83e-4c17-b005-748946772e09 · outbound

This paper cites Universal adversarial perturbations,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Universal adversarial perturbations,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.170302Z

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.

Observation e98a64f9-7b7d-40db-817c-3a6eee6bc7e5 · outbound

This paper cites Lavan: Localized and visible adversarial noise,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Lavan: Localized and visible adversarial noise,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.161319Z

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.

Observation e4cc175f-bdc4-4c3a-a961-10be1ab636ed · outbound

This paper cites Adversarial vulnerability of temporal feature networks for object detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Adversarial vulnerability of temporal feature networks for object detection,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.152890Z

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.

Observation 8b3dc529-5e36-43ca-93f9-794d0ae9ddfd · outbound

This paper cites Feasibility of incon- spicuous gan-generated adversarial patches against object detection,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Feasibility of incon- spicuous gan-generated adversarial patches against object detection,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.144130Z

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.

Observation 38509f7e-d59c-436f-80a0-430eb342bacf · outbound

This paper cites Patch-based attack on traffic sign recognition,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Patch-based attack on traffic sign recognition,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.134565Z

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.

Observation a09b0d95-001c-4678-9494-d9204b415630 · outbound

This paper cites Cyber attacks on scada based traffic light control systems in the smart cities,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Cyber attacks on scada based traffic light control systems in the smart cities,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.125236Z

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.

Observation ac9cdb83-9a5c-4b36-bb67-e49a149b62f6 · outbound

This paper cites Green lights forever: Analyzing the security of traffic infrastructure,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Green lights forever: Analyzing the security of traffic infrastructure,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.116399Z

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.

Observation 4b32bc11-5fb1-4203-8ca2-3aa3dd59f163 · outbound

This paper cites Fooling perception via location: a case of region-of-interest attacks on traffic light detection in au- tonomous driving,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Fooling perception via location: a case of region-of-interest attacks on traffic light detection in au- tonomous driving,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.107100Z

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.

Observation 18c31f7d-2671-4999-b9a1-a808aa01e4f7 · outbound

This paper cites Exposing congestion attack on emerging connected vehicle based traffic signal control.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Exposing congestion attack on emerging connected vehicle based traffic signal control

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.097716Z

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.

Observation c5eece71-0f99-4378-8ae2-03f53363c887 · outbound

This paper cites Secure traffic lights: Replay attack detection for model-based smart traffic con- trollers,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Secure traffic lights: Replay attack detection for model-based smart traffic con- trollers,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.088793Z

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.

Observation 25c7ed25-d2c8-4739-9adf-d3f0f14dd3d9 · outbound

This paper cites I can see the light: Attacks on autonomous vehicles using invisible lights,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors I can see the light: Attacks on autonomous vehicles using invisible lights,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.079344Z

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.

Observation 0b62d41f-d3e6-4fe8-8965-c6ee572a37bb · outbound

This paper cites Baidu apollo team (2017), apollo: Open source autonomous driving,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Baidu apollo team (2017), apollo: Open source autonomous driving,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.070484Z

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.

Observation 5dbfab1c-5b9b-4c6e-b59d-7620859edf40 · outbound

This paper cites BDD100K: A diverse driving dataset for heterogeneous multitask learning,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors BDD100K: A diverse driving dataset for heterogeneous multitask learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.061831Z

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.

Observation 113ca649-1dc1-43fa-8c46-2f5c388b2db4 · outbound

This paper cites Autoware on board: Enabling autonomous vehicles with embedded systems,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Autoware on board: Enabling autonomous vehicles with embedded systems,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.053086Z

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.

Observation 5ff093cc-f37e-42f1-a549-b2a6830aaee2 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:37:15.585881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eb276da-852f-4ad2-a0b4-0cedf947191d · outbound

This paper cites Rethink- ing the inception architecture for computer vision,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Rethink- ing the inception architecture for computer vision,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.044268Z

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.

Observation 9dd116f0-bded-4dd3-a22a-7614a4e55626 · outbound

This paper cites Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.035070Z

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.

Observation 0b187d66-f89c-433a-8dd7-ee66f3c42bca · outbound

This paper cites Exploring the Landscape of Spatial Robustness.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Exploring the Landscape of Spatial Robustness

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T10:37:16.019987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eeaa45e-60cb-4b07-bce3-a827f6769bad · outbound

This paper cites One pixel attack for fooling deep neural networks,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors One pixel attack for fooling deep neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.025609Z

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.

Observation 87c80969-7af9-4058-abc4-2fead663f26f · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Towards evaluating the robustness of neural networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.015742Z

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.

Observation 8a7db17d-ddbe-4f83-8c25-e2bd0aadb8f4 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Very deep convolutional networks for large-scale image recognition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:18.005468Z

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.

Observation a40a06aa-8b25-4a84-930e-9d63e57f2c96 · outbound

This paper cites Carla: An open urban driving simulator,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Carla: An open urban driving simulator,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.995424Z

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.

Observation 51c80d3f-f055-4150-9324-bf4baa473c8e · outbound

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

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Towards Deep Learning Models Resistant to Adversarial Attacks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.985715Z

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.

Observation 06a7c3e0-3f37-4f28-97cd-93369acb7161 · outbound

This paper cites TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:37:17.865443Z

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.

Observation f048c503-8e80-4f81-b8b1-3ec2f0d5e06a · outbound

This paper cites Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.975525Z

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.

Observation 9093ce40-e207-4cda-a4c1-4e9b6836460e · outbound

This paper cites Adam: A method for stochastic opti- mization,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Adam: A method for stochastic opti- mization,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.965511Z

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.

Observation 8eed12ef-b56c-45f8-af11-4eed65c060d7 · outbound

This paper cites Synthesizing robust adversarial examples,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Synthesizing robust adversarial examples,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.955887Z

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.

Observation 60ff88c6-b72c-4886-9c65-c04e9587339f · outbound

This paper cites On Physical Adversarial Patches for Object Detection.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors On Physical Adversarial Patches for Object Detection

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T10:37:17.197850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ca1dd0a-14fb-4d89-828e-088a26f1f977 · outbound

This paper cites Yolov7: Trainable bag- of-freebies sets new state-of-the-art for real-time object detectors,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Yolov7: Trainable bag- of-freebies sets new state-of-the-art for real-time object detectors,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.946620Z

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.

Observation 9e158376-8c81-4c47-a32d-1d9b122c72c9 · outbound

This paper cites Yolo by ultralytics (version 8.0.0) [computer software],.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Yolo by ultralytics (version 8.0.0) [computer software],

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.936416Z

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.

Observation 77d3635a-872c-4899-8ddc-4a07d75dade6 · outbound

This paper cites A deep analysis of the existing datasets for traffic light state recognition,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors A deep analysis of the existing datasets for traffic light state recognition,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.926794Z

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.

Observation 29a2425e-225f-4b0d-b114-f89229a01d06 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.917394Z

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.

Observation a70b0229-f7da-4245-bff1-16b98da59020 · outbound

This paper cites CoCar NextGen: a Multi-Purpose Platform for Con- nected Autonomous Driving Research,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors CoCar NextGen: a Multi-Purpose Platform for Con- nected Autonomous Driving Research,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.907459Z

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.

Observation fa8b2308-b648-4cd3-9482-98b50a0eaa2d · outbound

This paper cites The atlas of traffic lights: A reliable perception framework for autonomous driving,.

Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors The atlas of traffic lights: A reliable perception framework for autonomous driving,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:37:17.897088Z

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.

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