Paper Citation Record · LEDGER

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation

As of 14 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2412.16651.

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

pith.paper-citation-record.v1
2412.16651 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:26:30.873480Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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 fuzzy34
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3888836a-1fc5-4f3d-bcb5-49d8513e97c0 · outbound

This paper cites Baseg: Boundary aware semantic segmenta- tion for autonomous driving,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Baseg: Boundary aware semantic segmenta- tion for autonomous driving,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.536138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0873b16-f2c0-4f8c-89e2-35b186d11dc2 · outbound

This paper cites Medical image segmentation using deep neural networks with pre-trained encoders,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Medical image segmentation using deep neural networks with pre-trained encoders,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.522352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d96460e9-8bc7-444e-b85f-ad74478c227c · outbound

This paper cites Incorporating deeplabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Incorporating deeplabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.508235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 313faae9-6c60-4cdf-aed5-f4560dba3ee1 · outbound

This paper cites Pyramid scene parsing network,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Pyramid scene parsing network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.493339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation db2e9831-9921-418b-bbc6-4fda746d45ed · outbound

This paper cites Semantic image segmentation with deep convolutional nets and fully connected crfs,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Semantic image segmentation with deep convolutional nets and fully connected crfs,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.479031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4e173e81-a609-43c5-abe8-0deb4fcd7f71 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.465337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff8f95a5-8139-4c1a-a8c7-88056ef6d2d5 · outbound

This paper cites Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.452262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d65ea725-7de5-46d5-ac54-ca5c5bad89b2 · outbound

This paper cites TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T10:26:30.747431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a9abad4-e971-4a4b-9630-7ce1224eb919 · outbound

This paper cites Transferable adversarial facial images for privacy protection,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Transferable adversarial facial images for privacy protection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.438729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3519a876-3a65-45b5-bcdb-ebea049cd757 · outbound

This paper cites Numbod: A spatial-frequency fusion attack against object detectors,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Numbod: A spatial-frequency fusion attack against object detectors,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.426190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 564a74e7-19c7-4afa-8aaf-82c0a4907dd5 · outbound

This paper cites Adversarial machine learning in image classification: A survey toward the defender’s perspective,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Adversarial machine learning in image classification: A survey toward the defender’s perspective,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.412182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ab17ed68-ee2c-4baa-878b-2602fec3f807 · outbound

This paper cites Downstream-agnostic adversarial examples,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Downstream-agnostic adversarial examples,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.397939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b2ae835e-45a5-4c59-95da-68f7e3c29c27 · outbound

This paper cites Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.383700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 49ed2eb4-affe-478c-90f3-0ca92cca2014 · outbound

This paper cites Darksam: Fooling segment anything model to segment nothing,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Darksam: Fooling segment anything model to segment nothing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.369132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15914f7b-69b3-45b4-a16f-788a43ce6cd1 · outbound

This paper cites Universal adversarial perturbations against semantic image segmentation,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Universal adversarial perturbations against semantic image segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.355413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f5f0766-8a5d-43bf-8c7d-3e712d3d2dd9 · outbound

This paper cites Improving transferability of generated universal adversarial perturbations for image classification and segmentation,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Improving transferability of generated universal adversarial perturbations for image classification and segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.341489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d2077bd6-0d6c-4c7a-99fa-7c02597b4353 · outbound

This paper cites Universal adversarial perturbations,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Universal adversarial perturbations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.328143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e00b12e-51ab-476e-ba32-50251d873695 · outbound

This paper cites Universal adversarial attack via enhanced projected gradient descent,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Universal adversarial attack via enhanced projected gradient descent,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.315189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 07877c87-44cd-431f-9db6-4e499e9425d3 · outbound

This paper cites Prototype and context-enhanced learning for unsupervised domain adaptation semantic segmentation of remote sensing images,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Prototype and context-enhanced learning for unsupervised domain adaptation semantic segmentation of remote sensing images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.301554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cfe01e04-9b11-42bb-a8f9-08417a308ca9 · outbound

This paper cites Fpanet: Feature pyramid aggregation network for real-time semantic segmenta- tion,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Fpanet: Feature pyramid aggregation network for real-time semantic segmenta- tion,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.288258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 652a8765-4137-4b03-9ab1-e3d43ab8629f · outbound

This paper cites An encoder-decoder network based fcn architecture for semantic segmentation,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation An encoder-decoder network based fcn architecture for semantic segmentation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.272799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30b26ec2-28f7-4f49-991a-55bf7bfba86c · outbound

This paper cites Cgnet: A light-weight context guided network for semantic segmenta- tion,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Cgnet: A light-weight context guided network for semantic segmenta- tion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.257139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation db8913c9-ed9e-4d1b-9a93-4fe4709d5df1 · outbound

This paper cites Forest segmentation with spatial pyramid pooling modules: a surveillance system based on satellite images,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Forest segmentation with spatial pyramid pooling modules: a surveillance system based on satellite images,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.242536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3b69408-82a6-4a22-972e-7be87d0e0020 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T10:26:30.818258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcce9198-7144-4506-8abf-d88d72db35c6 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.227464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 31504737-6b8a-4b7d-ad7e-8bd88dcdac8b · outbound

This paper cites Denial-of-service or fine-grained control: Towards flexible model poisoning attacks on federated learning,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Denial-of-service or fine-grained control: Towards flexible model poisoning attacks on federated learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.211989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c5637817-9650-4c53-9d14-a41112015d74 · outbound

This paper cites Unlearnable 3d point clouds: Class-wise transformation is all you need,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Unlearnable 3d point clouds: Class-wise transformation is all you need,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.196687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0f050c6e-6f31-4815-b570-7730f8b53f8f · outbound

This paper cites Badhash: Invisible backdoor attacks against deep hashing with clean label,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Badhash: Invisible backdoor attacks against deep hashing with clean label,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.180805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc034115-fa3a-4144-96a1-caf0aa80a450 · outbound

This paper cites Detector collapse: Backdooring object detection to catastrophic overload or blindness,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Detector collapse: Backdooring object detection to catastrophic overload or blindness,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.165275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e8d47471-a0dc-493e-aeb3-8af88cb5ac0c · outbound

This paper cites Trojanrobot: Backdoor attacks against robotic manipulation in the physical world,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Trojanrobot: Backdoor attacks against robotic manipulation in the physical world,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T10:26:30.842775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cab9cb5a-94d6-4f1f-bb59-2f87e31fd71e · outbound

This paper cites Reverse backdoor distillation: Towards online backdoor attack detection for deep neural network models,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Reverse backdoor distillation: Towards online backdoor attack detection for deep neural network models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.150082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 83301a7a-1f6d-4b8b-8feb-2e45a9cc4559 · outbound

This paper cites BadRobot: Jailbreaking Embodied LLM Agents in the Physical World.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation BadRobot: Jailbreaking Embodied LLM Agents in the Physical World

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:26:30.850367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3160237d-6436-4efc-9f20-5caa834757f0 · outbound

This paper cites Breaking barriers in physical-world adversarial examples: Improving robustness and transferability via robust feature,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Breaking barriers in physical-world adversarial examples: Improving robustness and transferability via robust feature,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.133717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6fae2a93-0602-49e7-bd8e-85404ac33029 · outbound

This paper cites Data-free universal adversarial perturbation and black-box attack,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Data-free universal adversarial perturbation and black-box attack,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.117737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35309963-2c6e-489e-ad95-a07c565d5bc6 · outbound

This paper cites Frequency-driven imperceptible adversarial attack on semantic similarity,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Frequency-driven imperceptible adversarial attack on semantic similarity,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.103397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eddad27c-3026-4d2c-a912-e08c205fd98b · outbound

This paper cites The pascal visual object classes (voc) challenge,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation The pascal visual object classes (voc) challenge,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.088964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cf32e2f2-0df3-4f5d-9379-99b4dc37e0bb · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation The cityscapes dataset for semantic urban scene understanding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.075347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ba4a65e-5ce8-4775-a78f-826b442ae8fb · outbound

This paper cites Securely fine-tuning pre-trained encoders against adversarial examples,.

PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation Securely fine-tuning pre-trained encoders against adversarial examples,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:26:31.061211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.