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Paper Citation Record · LEDGER

Black-box Adversarial Attacks on CNN-based SLAM Algorithms

As of 8 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:49.888835Z digest=sha256:4877873a9136576d7aa18838216520df3e929bdda62112035a350d0272120d8f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:49.931206Z digest=sha256:82412852d2b8d4dff58a81054608965ef11adc984661b8502c5de9d9a6b756ec

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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no resolver link, observed 2026-08-07T12:18:49.993940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:49.993940Z digest=sha256:cce68af71e97e663e89a6ebd1b9c80d88df03937ef5971d465fd93459981aeaa

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.061516Z digest=sha256:fda57bb9ef529bb8b46ae0ee832cc81747b6ffbef2455c80de1cd29d383f7a2b

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.151879Z digest=sha256:d7a8995d2c4df69ec2c395278a5286e7255c5333014d27e5e35df16ce9d80d1d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.329208Z digest=sha256:c91650b4cbf9cf14bbf5c7c676318a7877473b158dae223900ab7820f7c50f7a

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=sha256:9fdffb96c12c02bed0abe9d8f832bd373a3cb0fc37653ba55ca4c295c1054001

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
verified fuzzy
raw_fallback, observed 2026-08-07T12:18:58.666774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.575072Z digest=sha256:9ac092a59d37e968f3ac3646f47751eb9c99e3bd17d1c9aabcd462c319e1bf7c

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=sha256:4d38236a8fb631cd848e965e5007951e8e26a87b8718fc339623f87603645f94

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.769661Z digest=sha256:2fb3990e1c35cb04a08a943226b358ae844fe7c1caf3243bc4d6d9abf3b16e62

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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no resolver link, observed 2026-08-07T12:18:50.859519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:50.859519Z digest=sha256:9158450c740712e6f2c135c9550976657880c8f6130a8f5c6453fceee5221bbe

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:50.948585Z digest=sha256:8efab5af589b4e900a93d355a014171e59c356ca5a67613de3945951e0078704

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
verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.018390Z digest=sha256:e743968920b1c95dc47a693605ae4cd4ffcc5698fcf292e63f7c0333980c15af

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.114583Z digest=sha256:ca05b514fdbadd070c7226be29b72adee6b09a357037e088afb072253fc84cea

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
verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.191171Z digest=sha256:103d92666d12d73517bf0df3634538538df2525ca5915f1f167bdd618c327a97

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.301759Z digest=sha256:dab10bb1f13113839f43c3d7ca9b208152a280433ca284f786ef167286a1088e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.365048Z digest=sha256:787ae25efd4165b047c5b65273f13b524c38c5f201f95b6e439182d683b629d6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.425474Z digest=sha256:64bb00350ec1c8b74d5548db05eb25ccc988bdd6d6729cf14353f4bfac5f1ecd

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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no resolver link, observed 2026-08-07T12:18:51.517649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:18:51.517649Z digest=sha256:1da3856e6f2b69a1bb8ad700116866ab2f9cd5d65a17d8df8b4732c90953b8c7

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.591518Z digest=sha256:616f8a78345616b24e822efa81def0665a69129760a420d5128375a7d87c7337

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:a74bfcf46e8351bda6a140731f1a9c3708b9dfe0ef4dbf04d055e1eadcac8289

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.736753Z digest=sha256:f29c831a5dbb3702c887949e8b3474104a6e41e2d9a27dec92fb26e146b314fc

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=sha256:66f6c9b030434d3cbd242db7d60473491fc7fba8c202be566294e779b8834f57

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.928571Z digest=sha256:8858178f645e203de10032552e21819babdef0ed801100d36b4bbb209dd55886

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:51.991143Z digest=sha256:7643cd28df8c9eab3a958f7bfe570a176e61f12e85318f2a7d5c4e0cb973401d

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=sha256:7796858814f194bd2ece83031078fc1b0d40db494d450a046e058ca2218a7dfd

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.160439Z digest=sha256:0c2ae3f9c4bec19104a02cd35e9ac4c9f80821c2503fa5a5ad58afb558f50e8f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.255857Z digest=sha256:164d15bac3a52fdff707e9a62a47316e3523f5c703997136f6eebd5e0e16c9ac

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.345028Z digest=sha256:70070c3cb34a46bab5f71cbd17868cd658198217964070258bf1991459bacda5

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.418767Z digest=sha256:2675278c3eb2c5c4829585079013b24c8f6d2a35da0515436da4747926ee681e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.491043Z digest=sha256:874539bae8954b4f30ead5b4ef2233cf2a99a5ebc4667040796813b4b94317aa

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.619768Z digest=sha256:80f3c4c470bb79fabcf9367ff42a39211392a98c89deb701f36afe40b2c4c960

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.743148Z digest=sha256:afd9570f610dbddcb1c26dea659ac5ccf34c5315da1bf55ba01925b77db66962

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.809886Z digest=sha256:6ace50348cda2200be7817fc2faa665cff70103389ea17e512f99d9cee4206cf

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.864051Z digest=sha256:d51405e1d0d45a05cd3bf69f46df84679be9a166ac8732cd07a4a877607272db

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:52.958559Z digest=sha256:ef097dbd462bcd67d9bfaee3a612f54de02a3f25d8959f170312556d40f1e8e0

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:53.017249Z digest=sha256:731fb6eb6d57fa0259b1de35d23ae841b3339ef228d95143f77421c8297a7cb0

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:18:53.078471Z digest=sha256:5046ab6d3e63a3da4e57a99edd2744e4492cf27e040976b21c0ede2f9e41a78d

Pith citing papers

No inbound Pith citation observations are available.