Paper Citation Record · LEDGER

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2502.08079.

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

pith.paper-citation-record.v1
2502.08079 v3

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:56:49.365745Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:46:56.552601Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:09:54.631580Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy49
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2848a6b5-2686-47f3-ad3e-61d93108a717 · outbound

This paper cites write newline.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models write newline

Reference 1

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Observation 76f46599-2a57-41ca-bd95-a020862e55f9 · outbound

This paper cites Boosting adversarial attacks with momentum.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Boosting adversarial attacks with momentum

Reference 2

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Observation 3edb9ccf-bf08-4d55-83a1-453dfe52578a · outbound

This paper cites Evading defenses to transferable adversarial examples by translation-invariant attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Evading defenses to transferable adversarial examples by translation-invariant attacks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.086956Z

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Observation 9d141539-66d4-401c-8c42-678e10a0322e · outbound

This paper cites An image is worth 16x16 words: transformers for image recognition at scale.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models An image is worth 16x16 words: transformers for image recognition at scale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.074681Z

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Observation d5e299df-70ec-4438-b0f5-929f071f641e · outbound

This paper cites Learning to learn transferable attack.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Learning to learn transferable attack

Reference 5

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Observation 36cd5bca-108d-4491-850f-53b6302c1030 · outbound

This paper cites Fda: Feature disruptive attack.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Fda: Feature disruptive attack

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.051470Z

Source-reported events for the cited work

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Observation 288e6807-85af-41c0-a910-1b0424aa098e · outbound

This paper cites Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory

Reference 7

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Observation 24c044c8-0174-49a2-9003-9321ec9c3ba3 · outbound

This paper cites SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 8

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Observation 77f04d8d-c1b9-4a9a-b5b2-a3b4531b8e0c · outbound

This paper cites Deep residual learning for image recognition.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Deep residual learning for image recognition

Reference 9

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Source-reported events for the cited work

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Observation 9c5a7f62-3d21-4168-af6f-e7ec28333298 · outbound

This paper cites Natural adversarial examples.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Natural adversarial examples

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.023038Z

Source-reported events for the cited work

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Observation 5687506a-7ff1-465e-80ed-32af63044224 · outbound

This paper cites Adversarial examples are not bugs, they are features.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Adversarial examples are not bugs, they are features

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.012858Z

Source-reported events for the cited work

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Observation 68ea14f5-97d5-4610-9d01-39bc5c29721e · outbound

This paper cites Transferable perturbations of deep feature distributions.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Transferable perturbations of deep feature distributions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:50.002161Z

Source-reported events for the cited work

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Observation 12e84fee-c2ff-4bb3-8560-c5ff50b30d21 · outbound

This paper cites Adversarial example generation with syntactically controlled paraphrase networks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Adversarial example generation with syntactically controlled paraphrase networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.991529Z

Source-reported events for the cited work

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Observation 312c34c4-0b5b-4120-9b73-341d470be727 · outbound

This paper cites Is bert really robust? a strong baseline for natural language attack on text classification and entailment.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Is bert really robust? a strong baseline for natural language attack on text classification and entailment

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.980878Z

Source-reported events for the cited work

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Observation 55e61711-0a94-4f6f-b8d7-c31189dba137 · outbound

This paper cites Align before fuse: vision and language representation learning with momentum distillation.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Align before fuse: vision and language representation learning with momentum distillation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.970142Z

Source-reported events for the cited work

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Observation 7f54841a-a7dd-4520-9282-d6993664ae9a · outbound

This paper cites Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.959748Z

Source-reported events for the cited work

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Observation c6da15fc-6387-4357-96ab-0d3676caba3a · outbound

This paper cites Bert-attack: adversarial attack against bert using bert.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Bert-attack: adversarial attack against bert using bert

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.948862Z

Source-reported events for the cited work

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Observation b002cfa9-840a-4733-829a-0ace8b9445d9 · outbound

This paper cites Nesterov accelerated gradient and scale invariance for adversarial attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Nesterov accelerated gradient and scale invariance for adversarial attacks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.938578Z

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Observation 96d0fde0-55d5-427e-ae05-dfda1b557751 · outbound

This paper cites Microsoft coco: common objects in context.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Microsoft coco: common objects in context

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.927870Z

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Observation bafd4ac0-2912-40bb-8aa9-b61eeb839ada · outbound

This paper cites Delving into transferable adversarial examples and black-box attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Delving into transferable adversarial examples and black-box attacks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.917282Z

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Observation c7ed4afb-0228-4fd4-b9bf-fecdff89fe51 · outbound

This paper cites On the convergence of an adaptive momentum method for adversarial attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models On the convergence of an adaptive momentum method for adversarial attacks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.906829Z

Source-reported events for the cited work

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Observation 2775cb01-e92c-45b8-8866-48197dd8f866 · outbound

This paper cites Set-level guidance attack: boosting adversarial transferability of vision-language pre-training models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Set-level guidance attack: boosting adversarial transferability of vision-language pre-training models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.895976Z

Source-reported events for the cited work

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Observation 81c48614-8602-4201-9922-5568b37eb5b3 · outbound

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

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Towards deep learning models resistant to adversarial attacks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.885014Z

Source-reported events for the cited work

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Observation fb12cd51-67f1-46e8-9db7-3cdead3ca969 · outbound

This paper cites Universal adversarial perturbations.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Universal adversarial perturbations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.874609Z

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Observation 3025e76c-7f95-483e-9469-4e98aad4bfa6 · outbound

This paper cites Stress test evaluation for natural language inference.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Stress test evaluation for natural language inference

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.864898Z

Source-reported events for the cited work

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Observation 3405c873-a85c-4cde-b3a3-b079bb87c7b1 · outbound

This paper cites Cross-domain transferability of adversarial perturbations.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Cross-domain transferability of adversarial perturbations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.855199Z

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Observation cb289f9d-d767-47b5-a6f3-8ee47f037b98 · outbound

This paper cites Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.845430Z

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Observation 4085db41-a8eb-4c4e-b4c2-4bea601d55bd · outbound

This paper cites Generative adversarial perturbations.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Generative adversarial perturbations

Reference 28

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Observation 5f0dcf48-10b8-4ac6-9699-b9382ae7a729 · outbound

This paper cites Understanding and improving robustness of vision transformers through patch-based negative augmentation.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Understanding and improving robustness of vision transformers through patch-based negative augmentation

Reference 29

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Observation d4be6507-252e-4405-98c6-e8350b275e75 · outbound

This paper cites Learning transferable visual models from natural language supervision.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Learning transferable visual models from natural language supervision

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.812991Z

Source-reported events for the cited work

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Observation 8f8fd580-1f33-4bfc-8a26-56f4e252420e · outbound

This paper cites Generating natural language adversarial examples through probability weighted word saliency.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Generating natural language adversarial examples through probability weighted word saliency

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.803054Z

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Observation bd1b5807-5191-4beb-be5e-f9a78cdd4fef · outbound

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

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Grad-cam: visual explanations from deep networks via gradient-based localization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.792921Z

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Observation c4439e56-3763-4940-a524-df6aa052b7f8 · outbound

This paper cites Intriguing properties of neural networks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Intriguing properties of neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.781913Z

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Observation 99c6b732-090c-4084-b246-8f5da860144b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models LLaMA: Open and Efficient Foundation Language Models

Reference 34

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Observation 0d3b3747-ed78-406a-9fd0-566f9406bb24 · outbound

This paper cites Boosting adversarial transferability by block shuffle and rotation.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Boosting adversarial transferability by block shuffle and rotation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.771753Z

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Observation 96a9a253-6bc9-4c96-aa3a-e1499b4b2917 · outbound

This paper cites Admix: enhancing the transferability of adversarial attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Admix: enhancing the transferability of adversarial attacks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.761626Z

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Observation f5d7deaa-4086-4c1a-b6a6-f07606e4263c · outbound

This paper cites Structure invariant transformation for better adversarial transferability.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Structure invariant transformation for better adversarial transferability

Reference 37

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Observation 6aa0cbd8-4caa-4d11-b329-16a74158483d · outbound

This paper cites Prototype-supervised adversarial network for targeted attack of deep hashing.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Prototype-supervised adversarial network for targeted attack of deep hashing

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.741500Z

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Observation f6dbda94-6773-481f-bdc3-e92eb54b30bd · outbound

This paper cites Enhancing the self-universality for transferable targeted attacks.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Enhancing the self-universality for transferable targeted attacks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.731545Z

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Observation fda6f140-8c2e-4852-b375-094502d72b81 · outbound

This paper cites Improving transferability of adversarial examples with input diversity.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Improving transferability of adversarial examples with input diversity

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.721517Z

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Observation 5e71f3d3-3b59-457b-967e-1be82a75409c · outbound

This paper cites Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.711242Z

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Observation ecaaa87b-9ffa-4209-9822-279fc5beb2d9 · outbound

This paper cites Fooling vision and language models despite localization and attention mechanism.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Fooling vision and language models despite localization and attention mechanism

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.700452Z

Source-reported events for the cited work

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Observation 888fc5f3-6fb5-42b2-b3d7-5ee2e8dc7c6c · outbound

This paper cites Vision-language pre-training with triple contrastive learning.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Vision-language pre-training with triple contrastive learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.690292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c2dfbaf8-318c-4e0f-b4aa-67d4b4a51b9c · outbound

This paper cites Vlattack: multimodal adversarial attacks on vision-language tasks via pre-trained models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Vlattack: multimodal adversarial attacks on vision-language tasks via pre-trained models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.678987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation edf623ac-c007-4737-944f-190f137a897b · outbound

This paper cites Modeling context in referring expressions.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Modeling context in referring expressions

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.667614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5741ff4e-e0cb-4eab-90a1-71231416a415 · outbound

This paper cites Towards adversarial attack on vision-language pre-training models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Towards adversarial attack on vision-language pre-training models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.657490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cb8b687b-c41a-4492-97b3-78c5506068f3 · outbound

This paper cites A survey on image perturbations for model robustness: Attacks and defenses.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models A survey on image perturbations for model robustness: Attacks and defenses

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.645257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f07c36e4-e152-4c8c-b78b-ac0dca51c499 · outbound

This paper cites Privacy protection in deep multi-modal retrieval.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Privacy protection in deep multi-modal retrieval

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.632590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b302deb-489f-498b-8e52-090aacccef3a · outbound

This paper cites Proactive privacy-preserving learning for cross-modal retrieval.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Proactive privacy-preserving learning for cross-modal retrieval

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.619918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8a5a04e2-34a2-4d74-a815-8a09114ab739 · outbound

This paper cites Universal adversarial perturbations for vision-language pre-trained models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Universal adversarial perturbations for vision-language pre-trained models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.607854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 76631142-aaed-4cdb-94b7-66bc0c74de0f · outbound

This paper cites Adversarial robustness through the lens of causality.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Adversarial robustness through the lens of causality

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.597241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cc264f0a-8933-4f94-8e33-f196550a1a5e · outbound

This paper cites On evaluating adversarial robustness of large vision-language models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models On evaluating adversarial robustness of large vision-language models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.585539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fbc2d895-c296-4e22-a100-2e5f2d1a991c · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T10:56:49.347950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 673301c2-5dba-4ba0-a2f9-88789fb754b6 · outbound

This paper cites Efficient query-based black-box attack against cross-modal hashing retrieval.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Efficient query-based black-box attack against cross-modal hashing retrieval

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:56:49.572974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 15c5ac08-355d-4e85-870c-cd0bfcb83864 · outbound

This paper cites @esa (Ref.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models @esa (Ref

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T10:56:49.356705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53e65c87-201d-4ac7-9b45-ebd2575f6f8c · outbound

This paper cites an unresolved cited work.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T10:56:49.361124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee93ebd-370d-4942-9946-b4ea05a46028 · outbound

This paper cites X"bDls= L9 l֡33*!ںj@vp?3m endstream endobj 26 0 obj << /Filter /FlateDecode /Length 249 >> stream xMQI 0 @!^CC 9 X 1 ,=!s7 ٻYz.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models X"bDls= L9 l֡33*!ںj@vp?3m endstream endobj 26 0 obj << /Filter /FlateDecode /Length 249 >> stream xMQI 0 @!^CC 9 X 1 ,=!s7 ٻYz

Reference 57

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T10:56:49.546357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation bba5a8e3-a9f4-4f1b-be03-28d2c46f2d24 · inbound

MIRAGE: Protecting against Malicious Image Editing via False Moderation cites this paper.

MIRAGE: Protecting against Malicious Image Editing via False Moderation MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:09:54.633145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6bcb7d15-d7b4-44d2-8247-8bed0bdeee45 · inbound

MIRAGE: Protecting against Malicious Image Editing via False Moderation cites this paper.

MIRAGE: Protecting against Malicious Image Editing via False Moderation MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:53.539382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.