Attacking Attention of Foundation Models Disrupts Downstream Tasks | Paper Citation Record

Observation 7626cca1-6979-4450-a614-393ab109a233 · outbound

This paper cites Reveal of Vision Transformers Robustness against Adversarial Attacks.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Reveal of Vision Transformers Robustness against Adversarial Attacks

Reference 1

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This paper cites Are transformers more robust than cnns?Advances in neural information processing systems, 34:26831–26843, 2021.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Are transformers more robust than cnns?Advances in neural information processing systems, 34:26831–26843, 2021

Reference 2

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This paper cites Under- standing robustness of transformers for image classification.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Under- standing robustness of transformers for image classification

Reference 3

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This paper cites Language Models are Few-Shot Learners.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Language Models are Few-Shot Learners

Reference 4

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This paper cites Towards evaluating the robustness of neural networks.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Towards evaluating the robustness of neural networks

Reference 5

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Observation 4fc3cd5e-2f00-4856-a911-12e536c0ae9d · outbound

This paper cites Poisoning Web-Scale Training Datasets is Practical.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Poisoning Web-Scale Training Datasets is Practical

Reference 6

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Emerg- ing properties in self-supervised vision transformers

Reference 7

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Attacking Attention of Foundation Models Disrupts Downstream Tasks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

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Observation 2e9d71c7-e312-4234-a5d2-271fd261be2a · outbound

This paper cites Boosting adversarial at- tacks with momentum.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Boosting adversarial at- tacks with momentum

Reference 9

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Observation 793f91ff-fa44-43c0-a7d7-f9499318a575 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Attacking Attention of Foundation Models Disrupts Downstream Tasks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 56bb9b5a-5e2c-4e93-b17b-69f6e8bfe76e · outbound

This paper cites Adversarial examples for the openai clip in its zero-shot classification regime and their semantic gener- alization, 2021.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Adversarial examples for the openai clip in its zero-shot classification regime and their semantic gener- alization, 2021

Reference 11

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Observation f081d8eb-f314-4e95-a9ad-4cdba02ff7d0 · outbound

This paper cites Pixels still beat text: Attacking the openai clip model with text patches and adversarial pixel perturbations,.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Pixels still beat text: Attacking the openai clip model with text patches and adversarial pixel perturbations,

Reference 12

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This paper cites Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?

Reference 13

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Observation 00ef6cb9-51bf-409e-903a-0f7fd95a8826 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Explaining and Harnessing Adversarial Examples

Reference 14

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This paper cites Are vision trans- formers robust to patch perturbations? InEuropean Con- ference on Computer Vision, pages 404–421.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Are vision trans- formers robust to patch perturbations? InEuropean Con- ference on Computer Vision, pages 404–421

Reference 15

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Observation 23f999c7-dc3c-40cf-a482-00828550a3bc · outbound

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 16

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Observation 1dd8f011-0023-44fa-82e6-9b7d06bbcbfd · outbound

This paper cites Black-box adversarial attacks with limited queries and information.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Black-box adversarial attacks with limited queries and information

Reference 17

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Observation 6065268d-1d4a-47f2-bc12-2f7a41243596 · outbound

This paper cites Scal- ing up vision-language pretraining.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Scal- ing up vision-language pretraining

Reference 18

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Observation d1ce4c97-b8b6-4eee-b007-69eb45138b1e · outbound

This paper cites Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective

Reference 19

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Observation b3972a62-6d42-458f-b5e3-c7f924e66c48 · outbound

This paper cites Curved representation space of vision transformers.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Curved representation space of vision transformers

Reference 20

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This paper cites Segment any- thing.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Segment any- thing

Reference 21

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This paper cites Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks

Reference 22

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This paper cites Ad- versarial examples in the physical world.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Ad- versarial examples in the physical world

Reference 23

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021

Reference 24

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 25

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This paper cites Lawrence Zitnick, and Piotr Doll ´ar.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Lawrence Zitnick, and Piotr Doll ´ar

Reference 26

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This paper cites Exploring the Relationship between Architecture and Adversarially Robust Generalization.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Exploring the Relationship between Architecture and Adversarially Robust Generalization

Reference 27

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Observation a5b6df67-9b01-44d6-99dc-aa2bf7619cd8 · outbound

This paper cites Decoupled Weight Decay Regularization.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Decoupled Weight Decay Regularization

Reference 28

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This paper cites Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models

Reference 29

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 30

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This paper cites On the robustness of vision transformers to adversarial ex- amples.

Attacking Attention of Foundation Models Disrupts Downstream Tasks On the robustness of vision transformers to adversarial ex- amples

Reference 31

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Deepfool: a simple and accurate method to fool deep neural networks

Reference 32

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This paper cites On Improving Adversarial Transferability of Vision Transformers.

Attacking Attention of Foundation Models Disrupts Downstream Tasks On Improving Adversarial Transferability of Vision Transformers

Reference 33

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This paper cites Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons

Reference 34

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Attacking Attention of Foundation Models Disrupts Downstream Tasks DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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This paper cites Learning transferable visual models from natural language supervi- sion.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Learning transferable visual models from natural language supervi- sion

Reference 36

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This paper cites Berg, and Li Fei-Fei.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Berg, and Li Fei-Fei

Reference 37

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This paper cites On the Adversarial Robustness of Vision Transformers.

Attacking Attention of Foundation Models Disrupts Downstream Tasks On the Adversarial Robustness of Vision Transformers

Reference 38

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This paper cites Cnn features off-the-shelf: an astound- ing baseline for recognition.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Cnn features off-the-shelf: an astound- ing baseline for recognition

Reference 39

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This paper cites Indoor segmentation and support inference from rgbd images.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Indoor segmentation and support inference from rgbd images

Reference 40

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This paper cites Adversarial risk and the dangers of eval- uating against weak attacks.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Adversarial risk and the dangers of eval- uating against weak attacks

Reference 41

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This paper cites Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study

Reference 42

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This paper cites Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning

Reference 43

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This paper cites Towards transferable adversarial attacks on vision transformers.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Towards transferable adversarial attacks on vision transformers

Reference 44

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks Vision-language pre-training with triple contrastive learning

Reference 45

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Attacking Attention of Foundation Models Disrupts Downstream Tasks Unresolved cited work

Reference 46

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This paper cites Florence: A New Foundation Model for Computer Vision.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Florence: A New Foundation Model for Computer Vision

Reference 47

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks Towards adversarial attack on vision-language pre-training models

Reference 48

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Observation 849749a2-0af8-4f96-897b-6277acf5c793 · outbound

This paper cites Transferable adversarial attacks on vision transform- ers with token gradient regularization.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Transferable adversarial attacks on vision transform- ers with token gradient regularization

Reference 49

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks Univer- sal adversarial perturbations for vision-language pre-trained models

Reference 50

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Observation 2a16dad9-7676-4d23-94b8-a4dffcf7dbec · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.International Journal of Computer Vision, 127(3):302–321, 2019.

Attacking Attention of Foundation Models Disrupts Downstream Tasks Semantic under- standing of scenes through the ade20k dataset.International Journal of Computer Vision, 127(3):302–321, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:09:12.194890Z

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