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Paper Citation Record · LEDGER

Black-box Adversarial Attacks on CNN-based SLAM Algorithms

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.24654.

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

pith.paper-citation-record.v1
2505.24654 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:18:53.078471Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy28
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3250168-b669-4259-8490-fcdf074db9c2 · outbound

This paper cites Adversarial Attacks on Camera-Lidar Models for 3D Car Detection.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Adversarial Attacks on Camera-Lidar Models for 3D Car Detection

Reference 1

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raw_fallback, observed 2026-08-07T12:18:59.684784Z

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.

source=pdf_text observed=2026-08-07T12:18:49.888835Z digest=sha256:46063665e798ce6db4d3d3049d5c161e7103b861f9b45e6627dcf3e82556d07a

Observation bbcbd0ef-dbcc-4187-aae3-0e13481d2e3c · outbound

This paper cites an unresolved cited work.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Unresolved cited work

Reference 2

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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.

source=pdf_text observed=2026-08-07T12:18:49.931206Z digest=sha256:1e43e2b8ae0870dae9fe08ceabf03eb94547f2fedcdf85e4d515afcb2f2c28c8

Observation 49261d3a-3cf8-4395-816f-a75bbf5a6883 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:49.993940Z digest=sha256:121919543c842fd9fd06cc6a83121801c325d6cdb8bca7c7e1eeb071d8f382d0

Observation 3f654904-e4af-414b-a569-91ae8cbacd26 · outbound

This paper cites Towards Avaluating the Robustness of Neural Networks.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Towards Avaluating the Robustness of Neural Networks

Reference 4

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raw_fallback, observed 2026-08-07T12:18:59.234291Z

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.

source=pdf_text observed=2026-08-07T12:18:50.061516Z digest=sha256:e5202933ca6e89f56e833f5d50ff50b79e9395c77cceabced744ebdb1fefdc04

Observation 094fb51e-fba1-413f-9a12-ffbcfa9108ef · outbound

This paper cites Adversarial Attacks on Monocular Pose Estima- tion.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Adversarial Attacks on Monocular Pose Estima- tion

Reference 5

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raw_fallback, observed 2026-08-07T12:18:59.033143Z

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.

source=pdf_text observed=2026-08-07T12:18:50.151879Z digest=sha256:75915b391d9bd80bea26ef433505285b63a9a768f0051191a82d7cd710e79d93

Observation d70b09fc-1fda-45c4-918d-8347367efdfc · outbound

This paper cites ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:58.847017Z

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.

source=pdf_text observed=2026-08-07T12:18:50.329208Z digest=sha256:06e542f5cfc86277541d2b62a6aadb8e941e171ffc33d58d7983232f634742bb

Observation 81afbb57-bea2-4665-925f-3877345ce09b · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Imagenet: A large-scale hierarchical image database

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:50.454542Z digest=sha256:a4c8d7338acb421221588a293322f4ebc34b438ec42d4eda484d9b2bfc6f9663

Observation 9c2e0522-6199-4b20-bbbb-926c47eac1f8 · outbound

This paper cites Superpoint: Self-supervised Interest Point Detection and Description.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Superpoint: Self-supervised Interest Point Detection and Description

Reference 8

Resolution
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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.

source=pdf_text observed=2026-08-07T12:18:50.575072Z digest=sha256:b3f996e54a02b4383729a25ddf0578c263b7a62b077e4b538ea2ed39fea3bcff

Observation d5533e13-f492-4e5d-930a-c2a08fcf543b · outbound

This paper cites Adversarial Attacks Against Medical Deep Learning Systems.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Adversarial Attacks Against Medical Deep Learning Systems

Reference 9

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no resolver link, observed 2026-08-07T12:18:50.673947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:50.673947Z digest=sha256:a33637e6d7892b813851e52763503332f5531f4dda58e33a5da4a0609103d21f

Observation e58b2171-f037-43b6-9846-9f206f866403 · outbound

This paper cites Black-box Adver- sarial Attacks through Speech Distortion for Speech Emo- tion Recognition.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Black-box Adver- sarial Attacks through Speech Distortion for Speech Emo- tion Recognition

Reference 10

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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.

source=pdf_text observed=2026-08-07T12:18:50.769661Z digest=sha256:02f94d66488e46fb27ecda40bc1310db0036ceaf025ccae27a4440be3d88c0b8

Observation c5a6609f-4976-4952-84fe-97ae253bd35f · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Explaining and Harnessing Adversarial Examples

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:50.859519Z digest=sha256:4ee2392108b0b1735942c7e51b9c227d0a9ed792e58053d5d6d0e1b3a6c6252e

Observation 577cacd8-6f2b-42e4-babd-ff0b7dbaec17 · outbound

This paper cites Simple black-box Adversar- ial Attacks.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Simple black-box Adversar- ial Attacks

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:58.303512Z

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.

source=pdf_text observed=2026-08-07T12:18:50.948585Z digest=sha256:e9d179ed4f1a65f700b01f40b79761a0227c843d76c25b4fc7df91f0bf0e9823

Observation bd21c672-e092-44f9-9db0-c204c4ebb474 · outbound

This paper cites A CMA-ES-Based Adversarial Attack Against Black-Box Object Detectors.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms A CMA-ES-Based Adversarial Attack Against Black-Box Object Detectors

Reference 13

Resolution
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raw_fallback, observed 2026-08-07T12:18:58.117197Z

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.

source=pdf_text observed=2026-08-07T12:18:51.018390Z digest=sha256:62d0fc1ae5146e142a838475d690627568810f8e76b413d5dfe7e287497b6d55

Observation 583bdf3a-8068-4fe8-8aa8-779c5620ce76 · outbound

This paper cites Densely connected convolutional net- works.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Densely connected convolutional net- works

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:57.917254Z

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.

source=pdf_text observed=2026-08-07T12:18:51.114583Z digest=sha256:2e35fbbc314b9ce2a4b71a0bee4eded85fec731c6cb971541c5b81ec9c3db8cb

Observation 4f5760c3-30f5-4480-adc0-a63df445d2f5 · outbound

This paper cites Black-box Adversarial Attacks with Limited Queries and Information.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Black-box Adversarial Attacks with Limited Queries and Information

Reference 15

Resolution
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raw_fallback, observed 2026-08-07T12:18:57.721893Z

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.

source=pdf_text observed=2026-08-07T12:18:51.191171Z digest=sha256:a6e5d67d71e0fbcac1373b06815fb2a1847df66b9598d60689d81da8ad02b888

Observation f377c70d-7ebb-487f-b70a-d8ab34854237 · outbound

This paper cites Adversarial Attack and De- fense of Yolo Detectors in Autonomous Driving Scenarios.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Adversarial Attack and De- fense of Yolo Detectors in Autonomous Driving Scenarios

Reference 16

Resolution
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raw_fallback, observed 2026-08-07T12:18:57.569455Z

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.

source=pdf_text observed=2026-08-07T12:18:51.301759Z digest=sha256:37618e56f0c09c6753a70740e8ad412e5e777f22f94c8a6d8222ed63031bf20d

Observation 81e14a06-4fec-456e-ae87-506395cfaee0 · outbound

This paper cites Black-box Adversarial Attacks on Video Recognition Models.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Black-box Adversarial Attacks on Video Recognition Models

Reference 17

Resolution
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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.

source=pdf_text observed=2026-08-07T12:18:51.365048Z digest=sha256:3de4cc07e49528eed7179c6203112a1a5379e32f897aba5fe4baed313aa58901

Observation e5c38435-7005-46e8-a096-c2ed6047801b · outbound

This paper cites Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah

Reference 18

Resolution
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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.

source=pdf_text observed=2026-08-07T12:18:51.425474Z digest=sha256:60975c007c7f4731e2454189468a8de58d9c0741af0ffa499121070c2bcf6146

Observation d32ed197-0db6-4752-a653-b1bcbde11bde · outbound

This paper cites Patch of Invisibility: Natu- ralistic Black-box Adversarial Attacks on Object Detectors.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Patch of Invisibility: Natu- ralistic Black-box Adversarial Attacks on Object Detectors

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:51.517649Z digest=sha256:f327cb4cf839370b279f17ca2be610e97fb333831b6ff117d1c524c8b223cb92

Observation 8cbf4f66-f818-46f6-99de-790054afb24b · outbound

This paper cites DXSLAM: A Robust and Efficient Visual SLAM System with Deep Fea- tures.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms DXSLAM: A Robust and Efficient Visual SLAM System with Deep Fea- tures

Reference 20

Resolution
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raw_fallback, observed 2026-08-07T12:18:56.969476Z

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.

source=pdf_text observed=2026-08-07T12:18:51.591518Z digest=sha256:e8cfc98957b5b7b6b3142321550b34e9cfe295b062533fb78a19b8aa3cab0318

Observation f3df2d28-bf80-4879-9fe0-952fe9c6a1f4 · outbound

This paper cites LightGlue: Local Feature Matching at Light Speed.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms LightGlue: Local Feature Matching at Light Speed

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:51.653364Z digest=sha256:cecd6e3a89bfbe42ce7d5d342c0dfa64df1cb873a6a932d19722c3d808b91ff2

Observation 80e728f0-b791-453e-be98-d1e62090b197 · outbound

This paper cites DPATCH: An Adversarial Patch Attack on Object Detectors.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms DPATCH: An Adversarial Patch Attack on Object Detectors

Reference 22

Resolution
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raw_fallback, observed 2026-08-07T12:18:56.712731Z

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.

source=pdf_text observed=2026-08-07T12:18:51.736753Z digest=sha256:12522e64d2a8728a5a721b4d300d6815b787104f51b7357664a29c73dac0f5b8

Observation 376c242e-0cc0-4550-b292-1be1b0c52f87 · outbound

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

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 23

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no resolver link, observed 2026-08-07T12:18:51.836456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:51.836456Z digest=sha256:13f1e30e63006c4e57281658d63818e3c7bbacb6798557f3474323057f68809e

Observation 847667ba-9498-4118-831e-09175064f850 · outbound

This paper cites ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:56.523544Z

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.

source=pdf_text observed=2026-08-07T12:18:51.928571Z digest=sha256:fdfe36d1f663f26c9c6788bc718059cf235ea7a902da1ad54c1b0e68c5d08a5b

Observation d6e2119f-5f2f-4394-b4ec-550f9f7ae2a1 · outbound

This paper cites Physical Passive Patch Adversarial Attacks on Visual Odometry Systems.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Physical Passive Patch Adversarial Attacks on Visual Odometry Systems

Reference 25

Resolution
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raw_fallback, observed 2026-08-07T12:18:56.413967Z

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.

source=pdf_text observed=2026-08-07T12:18:51.991143Z digest=sha256:c3160f23e0bb42ee29265dd83764bc5ca25113b0b780fe73f1130aa662531c77

Observation e47d1c62-270b-4fbe-b74f-2ca34767a06a · outbound

This paper cites Technical Report on the CleverHans v2.1.0 Adversarial Examples Library.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T12:18:52.090577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:52.090577Z digest=sha256:1dc362382bf9cd5d2f033aaaa4f8b973580c942adb474a16e5943073d2fdffa3

Observation 5fa270c0-bba2-4421-afb6-e033dacc3f58 · outbound

This paper cites Prac- tical Black-box attacks Against Machine Learning.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Prac- tical Black-box attacks Against Machine Learning

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:56.272339Z

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.

source=pdf_text observed=2026-08-07T12:18:52.160439Z digest=sha256:1d83a0a8fd83ff6a1fa3c7a83c5c922d81a8e0f0c157e65b13089b02f513bcfa

Observation b8c6f6cf-c5c4-45cb-9a4b-0507245ca1a8 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:56.117750Z

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.

source=pdf_text observed=2026-08-07T12:18:52.255857Z digest=sha256:cb0b756fb9b0fabe47c9259e26dd0430b557e56dc9a88eb543846661e7f0c5f8

Observation 30350d3e-f8d8-43b9-bb85-3094279f2578 · outbound

This paper cites SuperGlue: Learning Feature Matching With Graph Neural Networks.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms SuperGlue: Learning Feature Matching With Graph Neural Networks

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:55.926080Z

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.

source=pdf_text observed=2026-08-07T12:18:52.345028Z digest=sha256:4c16aa5c5e6d64b5eb5698ccef700ba7d7d5e57308277a1c5b249113de0efa0f

Observation ed522c34-54fa-4267-beb5-7dd16741c7c7 · outbound

This paper cites A Benchmark for the Eval- uation of RGB-D SLAM Systems.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms A Benchmark for the Eval- uation of RGB-D SLAM Systems

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:55.754253Z

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.

source=pdf_text observed=2026-08-07T12:18:52.418767Z digest=sha256:788359f8b767c889b788ddf46a8702f52fdb8b22b663b23389f72ac73361e5cc

Observation 5423f905-4e04-4881-b795-7b26d2742e2f · outbound

This paper cites Loftr: Detector-free local feature matching with transformers.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Loftr: Detector-free local feature matching with transformers

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:55.529981Z

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.

source=pdf_text observed=2026-08-07T12:18:52.491043Z digest=sha256:09bfb6aa9141e3dd274b7704b3343922a35edf946c6bd20099c66d3406f1f3ea

Observation 84d81f97-ade8-4523-a8b0-5707a773faa0 · outbound

This paper cites an unresolved cited work.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Unresolved cited work

Reference 32

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unresolved
raw_fallback, observed 2026-08-07T12:18:55.344061Z

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.

source=pdf_text observed=2026-08-07T12:18:52.619768Z digest=sha256:5b85883fc884d5e2825e996e1cb37bdbf4a271380552991280813dcb16a851fb

Observation 713d389d-bb07-4001-b205-a3e3ae3dff55 · outbound

This paper cites GCNv2: Efficient Correspondence Prediction for Real-Time SLAM.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:55.176964Z

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.

source=pdf_text observed=2026-08-07T12:18:52.743148Z digest=sha256:05c988b8252f7c9eb59013d501c4c9b824d435dc98bcd0dc545d78764c8af467

Observation 45ef3905-28fc-416c-8370-5483f5867c94 · outbound

This paper cites MatchFormer: Interleaving Attention in Transformers for Feature Matching.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms MatchFormer: Interleaving Attention in Transformers for Feature Matching

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:54.796561Z

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.

source=pdf_text observed=2026-08-07T12:18:52.809886Z digest=sha256:c2ccb782f4fda4ece1d1c57352bf4e52b52f520005d73312a9b409c921604f2f

Observation 61cff703-1a85-45ef-b898-89d752e5907a · outbound

This paper cites Rodr´ıguez, and Jianhua Wang.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Rodr´ıguez, and Jianhua Wang

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:54.464022Z

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.

source=pdf_text observed=2026-08-07T12:18:52.864051Z digest=sha256:7bb01691d03dcf147d6dd5397073a3cfc71bbc762051f3986110371a244d9f95

Observation bef6e3c5-a96e-48a0-87b9-44f3357f1068 · outbound

This paper cites Uni- versal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Uni- versal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:54.186424Z

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.

source=pdf_text observed=2026-08-07T12:18:52.958559Z digest=sha256:a05c37020c8329478eb6bb4f9bf6d203f48b9b2d49a8ec16eda8e796d7ded826

Observation e910de6b-029c-42ec-b4ff-0d78c5ed1a60 · outbound

This paper cites Miss the point: Targeted adversarial Attack on Multiple Landmark Detection.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Miss the point: Targeted adversarial Attack on Multiple Landmark Detection

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:53.815173Z

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.

source=pdf_text observed=2026-08-07T12:18:53.017249Z digest=sha256:28f4faefaf3507c813eb338454f97c17232b3f4506ba23f1bd34fa50ddefb00d

Observation 327db7cc-ba85-493f-beb5-5c84a62ed665 · outbound

This paper cites Ad- versarial Examples: Attacks and Defenses for Deep Learn- ing.

Black-box Adversarial Attacks on CNN-based SLAM Algorithms Ad- versarial Examples: Attacks and Defenses for Deep Learn- ing

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:53.543353Z

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.

source=pdf_text observed=2026-08-07T12:18:53.078471Z digest=sha256:9e3c2d053c600643fbe335586b7fb95e7e2fc2a93fde9dcb03f0dc94731e22d8

Pith citing papers

No inbound Pith citation observations are available.