PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection | Paper Citation Record

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This paper cites Al-Jarrah, Mehrdad Dianati, Saber Fallah, David Oxtoby, and Alex Mouzakitis.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Al-Jarrah, Mehrdad Dianati, Saber Fallah, David Oxtoby, and Alex Mouzakitis

Reference 1

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PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Unresolved cited work

Reference 2

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Observation 6bfc5bf3-48cb-4d12-abf6-4f7c96896cce · outbound

This paper cites Adversarial Patch.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarial Patch

Reference 3

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PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Unresolved cited work

Reference 4

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Observation a1f74e93-c026-4038-9887-f00c37a6d59c · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 5

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Observation a86bd88e-9a34-41de-b40f-fa3bb9f7d5bd · outbound

This paper cites Class- aware robust adversarial training for object detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Class- aware robust adversarial training for object detection

Reference 6

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This paper cites Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, and Tom Goldstein.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, and Tom Goldstein

Reference 7

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This paper cites Certified adversarial robustness via randomized smoothing.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Certified adversarial robustness via randomized smoothing

Reference 8

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Observation a8af24e3-d3a3-4497-b187-8efa8c642769 · outbound

This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 9

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Observation 77435248-9831-4808-9b98-c99bd6ecab80 · outbound

This paper cites Histograms of oriented gra- dients for human detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Histograms of oriented gra- dients for human detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:15.812321Z

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Observation 8e6700ba-891c-45b9-9302-adada16e315d · outbound

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

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Imagenet: A large-scale hierarchical im- age database

Reference 11

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Observation 02d40b81-d431-4ab3-ad59-932a6aa5d6b3 · outbound

This paper cites Adversarially-aware robust object detector.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarially-aware robust object detector

Reference 12

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Observation b4f19e8b-8c1b-4d5e-ac0f-c640e72bac28 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 13

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Observation 447fa5d4-4333-4383-8a29-0fe5cefefca4 · outbound

This paper cites Deep residual learning for image recognition.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Deep residual learning for image recognition

Reference 14

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Observation 4e49c149-f127-4beb-8b12-4f332392fc3e · outbound

This paper cites Naturalistic physical adversarial patch for object detectors.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Naturalistic physical adversarial patch for object detectors

Reference 15

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Observation c925c260-7388-4efb-ad7b-234af26f4bcf · outbound

This paper cites Adversarial texture for fooling per- son detectors in the physical world.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarial texture for fooling per- son detectors in the physical world

Reference 16

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Observation f164054f-6c29-4e2d-ab9a-62eae4814b24 · outbound

This paper cites Physically realizable natural- looking clothing textures evade person detectors via 3d mod- eling.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Physically realizable natural- looking clothing textures evade person detectors via 3d mod- eling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:14.597918Z

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Observation 38dd6a8f-0526-4e21-a725-509a7f591842 · outbound

This paper cites T-SEA: transfer-based self-ensemble attack on object detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection T-SEA: transfer-based self-ensemble attack on object detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:14.427945Z

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Observation 771a7a6c-16ab-4290-b21a-d34d7e165227 · outbound

This paper cites Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches

Reference 19

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Observation 22d3d155-afef-4519-b62b-0a5a68cc3996 · outbound

This paper cites Ultralytics yolov8, 2023.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Ultralytics yolov8, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:14.245327Z

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Observation 4d382693-e74f-4c14-90b3-3e1458bec290 · outbound

This paper cites Defending physical adversarial attack on object detection via adversarial patch-feature energy.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Defending physical adversarial attack on object detection via adversarial patch-feature energy

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:14.062740Z

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This paper cites Yolov3 and yolov4: Multi- ple object detection for surveillance applications.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Yolov3 and yolov4: Multi- ple object detection for surveillance applications

Reference 22

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Observation 28054e79-8a94-46b2-afa1-e04ce1e1ef3a · outbound

This paper cites Goodfellow, and Samy Bengio.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Goodfellow, and Samy Bengio

Reference 23

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Observation 07cdd28b-d09d-42c8-81aa-c102d0a7c083 · outbound

This paper cites Perceptual adversarial robustness: Defense against unseen threat mod- els.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Perceptual adversarial robustness: Defense against unseen threat mod- els

Reference 24

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Observation 04bb17a9-e1d1-4f02-a73a-fae099bb32b9 · outbound

This paper cites Ni, and Lei Zhang.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Ni, and Lei Zhang

Reference 25

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Observation d8cd9aa5-c89e-468a-9cc0-cd024a915cfa · outbound

This paper cites Recognizing object by components with human prior knowledge enhances adversarial robustness of deep neural networks.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Recognizing object by components with human prior knowledge enhances adversarial robustness of deep neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:13.259755Z

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This paper cites Partimagenet++ dataset: Scaling up part-based models for robust recognition.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Partimagenet++ dataset: Scaling up part-based models for robust recognition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:13.095911Z

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Observation 701f148b-fa1c-4ecd-9bea-adc5471e880c · outbound

This paper cites Language-driven anchors for zero-shot ad- versarial robustness.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Language-driven anchors for zero-shot ad- versarial robustness

Reference 28

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Observation e5ac65ee-0485-4ae8-a6cc-f77ea72c11fa · outbound

This paper cites On the importance of backbone to the adversarial robustness of object detectors.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection On the importance of backbone to the adversarial robustness of object detectors

Reference 29

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This paper cites Adbm: Adver- sarial diffusion bridge model for reliable adversarial purifi- cation.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adbm: Adver- sarial diffusion bridge model for reliable adversarial purifi- cation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:12.557554Z

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Observation 082f8bf1-c191-4b39-ab5d-a13c4f96f862 · outbound

This paper cites Microsoft coco: Common objects in context.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Microsoft coco: Common objects in context

Reference 31

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

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Segment and complete: Defending object detectors against adversarial patch attacks with robust patch detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:12.152384Z

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Observation 6f5b8460-a052-4eab-959b-de8115fa78d2 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Towards deep learning models resistant to adversarial attacks

Reference 33

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Observation d942ec4d-72c3-4060-9b65-d0d679cf9c12 · outbound

This paper cites Meta adversarial training against universal patches.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Meta adversarial training against universal patches

Reference 34

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Observation d86f38a7-7dda-4a12-b329-e233c881d6f4 · outbound

This paper cites Khan, and Fatih Porikli.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Khan, and Fatih Porikli

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:11.543972Z

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Observation bff722c0-0d48-4848-9817-6802ce53553c · outbound

This paper cites Adversarial training against location-optimized adversarial patches.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarial training against location-optimized adversarial patches

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:11.365694Z

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Observation e5e6fcb8-4f9a-4c76-b3f8-ccb75266a238 · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Accelerating 3D Deep Learning with PyTorch3D

Reference 37

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Observation 10e1bdc8-0c74-4f9b-9550-e035b65f547a · outbound

This paper cites YOLOv3: An Incremental Improvement.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection YOLOv3: An Incremental Improvement

Reference 38

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This paper cites Girshick, and Jian Sun.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Girshick, and Jian Sun

Reference 39

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Observation 31ff40d9-1a77-40da-b88a-671f3387a832 · outbound

This paper cites Do adversarially robust im- agenet models transfer better? In Adv.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Do adversarially robust im- agenet models transfer better? In Adv

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:11.018887Z

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Observation a78d38d0-f98f-474c-9ee8-41714508c718 · outbound

This paper cites Dickerson, Christoph Studer, Larry S.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Dickerson, Christoph Studer, Larry S

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:10.834187Z

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Observation decf1037-7f1a-4ab8-baeb-bff6f3e15751 · outbound

This paper cites an unresolved cited work.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:43:10.659408Z

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Observation 847c00e2-7a99-4e76-861a-9e4adc3ac096 · outbound

This paper cites Goodfellow, and Rob Fergus.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Goodfellow, and Rob Fergus

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:10.439450Z

Source-reported events for the cited work

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Observation 365a5fdc-0edf-451f-91c6-df1219d13352 · outbound

This paper cites Abu-Ghazaleh, and Ihsen Alouani.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Abu-Ghazaleh, and Ihsen Alouani

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:10.235220Z

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Observation ee0391cf-2b16-49bd-9270-bf8977a40933 · outbound

This paper cites Fool- ing automated surveillance cameras: Adversarial patches to attack person detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Fool- ing automated surveillance cameras: Adversarial patches to attack person detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:10.048962Z

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Observation a29bf6d2-f664-42f2-9d5d-db66f09bce34 · outbound

This paper cites FCOS: fully convolutional one-stage object detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection FCOS: fully convolutional one-stage object detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.853233Z

Source-reported events for the cited work

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Observation ce7271f7-2042-4f5d-907a-3baa4aa777a4 · outbound

This paper cites On adaptive attacks to adversarial ex- ample defenses.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection On adaptive attacks to adversarial ex- ample defenses

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.666951Z

Source-reported events for the cited work

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Observation d90df58f-9702-42cf-8a68-3ebaa51e280f · outbound

This paper cites Re- visiting adversarial training at scale.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Re- visiting adversarial training at scale

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.478648Z

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Observation 39636e1a-1917-459a-b5d1-b34024c5bc02 · outbound

This paper cites Napguard: Towards detecting naturalistic adversarial patches.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Napguard: Towards detecting naturalistic adversarial patches

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.249848Z

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Observation c7d486ce-c92e-408a-80c6-1dbd501d7ff8 · outbound

This paper cites Defend- ing against physically realizable attacks on image classifica- tion.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Defend- ing against physically realizable attacks on image classifica- tion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:09.002887Z

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Observation d3403b4b-29fe-4097-8704-8bfa5d884a50 · outbound

This paper cites Making an invisibility cloak: Real world adversar- ial attacks on object detectors.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Making an invisibility cloak: Real world adversar- ial attacks on object detectors

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.745295Z

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Observation 9b723a50-26e1-4b0d-89cd-1777f75984bd · outbound

This paper cites Adversarial t-shirt! evading person detectors in a phys- ical world.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Adversarial t-shirt! evading person detectors in a phys- ical world

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.553905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88889d38-96cc-40bc-b190-d532563875d8 · outbound

This paper cites Patchzero: Defending against adversarial patch at- tacks by detecting and zeroing the patch.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Patchzero: Defending against adversarial patch at- tacks by detecting and zeroing the patch

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.416471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7bd28e32-b079-4034-a244-4460077dd2f8 · outbound

This paper cites Defending against uni- versal adversarial patches by clipping feature norms.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Defending against uni- versal adversarial patches by clipping feature norms

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.249497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44f28573-444e-4313-af8f-69cf548f8c2d · outbound

This paper cites Defending person detection against adversarial patch at- tack by using universal defensive frame.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Defending person detection against adversarial patch at- tack by using universal defensive frame

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:08.042998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4636cb90-72a7-447a-8241-f6e51e6b9d1e · outbound

This paper cites Towards adversarially ro- bust object detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Towards adversarially ro- bust object detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:07.883360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e648cbda-37d7-4237-b3bd-a33fa42cdb86 · outbound

This paper cites Boning, Inderjit S.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Boning, Inderjit S

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:07.699013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e701437-e2ce-4df8-90da-d9852e24d499 · outbound

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

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Xing, Laurent El Ghaoui, and Michael I

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:07.521995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b206fa32-7c7b-4f6f-8837-00288f20fb1d · outbound

This paper cites Object detection with deep learning: A review.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Object detection with deep learning: A review

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:07.344077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb368115-5fbe-4887-b25f-96fdede03bb9 · outbound

This paper cites Information distribution based defense against physical attacks on object detection.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Information distribution based defense against physical attacks on object detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:07.168203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12a7b496-7f15-428a-8418-7ab825f1391d · outbound

This paper cites In each step, it evaluates t candidate positions and selects the one with the highest loss value.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection In each step, it evaluates t candidate positions and selects the one with the highest loss value

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:06.992747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83a7cbd0-5e44-4527-8af1-c9e8b6f67e39 · outbound

This paper cites It first performs t steps to determine the patch location, followed by k PGD steps to generate the adver- sarial patch.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection It first performs t steps to determine the patch location, followed by k PGD steps to generate the adver- sarial patch

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:06.812199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d37d4b9f-14c3-482b-b268-3889d55bb47b · outbound

This paper cites It then updates the patch using I-FGSM [23], requiring a total of p + 1steps per iteration.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection It then updates the patch using I-FGSM [23], requiring a total of p + 1steps per iteration

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:06.631624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3432d9fd-ac13-4da6-8f75-40d4783a65ca · outbound

This paper cites It requires only a single forward/backward pass with FreeAT [41], irrespective of the number of targets, and thus it significantly reduces computational overhead.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection It requires only a single forward/backward pass with FreeAT [41], irrespective of the number of targets, and thus it significantly reduces computational overhead

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:06.463719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ad4b69b-e5da-4903-a2bb-089645cecc3c · outbound

This paper cites All” and “Person.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection All” and “Person

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:43:06.321071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 73245ae5-9b0c-4536-8623-72451d2d6a6a · outbound

This paper cites an unresolved cited work.

PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection Unresolved cited work

Reference 2022

Resolution: parse uncertain; no resolver link, observed 2026-08-06T21:43:00.492618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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