On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline | Paper Citation Record

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This paper cites Qwen2.5-VL Technical Report.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Qwen2.5-VL Technical Report

Reference 1

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Observation 091e9adc-e980-4cac-8050-512448dc2191 · outbound

This paper cites SceneTAP: Scene-coherent typographic adversarial planner against vision-language mod- els in real-world environments.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline SceneTAP: Scene-coherent typographic adversarial planner against vision-language mod- els in real-world environments

Reference 2

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This paper cites Boosting adversarial attacks with momentum.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Boosting adversarial attacks with momentum

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory

Reference 4

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This paper cites Countering adversarial images using input transformations.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Countering adversarial images using input transformations

Reference 5

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This paper cites OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

Reference 6

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 7

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This paper cites Feature space perturbations yield more transferable adversar- ial examples.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Feature space perturbations yield more transferable adversar- ial examples

Reference 8

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This paper cites Tsang, and Xiaochun Cao.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Tsang, and Xiaochun Cao

Reference 9

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Observation 88cb1f7c-4217-4a78-8aec-aeb640d6a596 · outbound

This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 10

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Align before fuse: Vision and language representation learning with momentum distillation

Reference 11

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline BLIP: Bootstrapping language-image pre-training for unified vision- language understanding and generation

Reference 12

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This paper cites BERT-Attack: Adversarial attack against BERT using BERT.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline BERT-Attack: Adversarial attack against BERT using BERT

Reference 13

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Observation d3eaa9f5-cede-4482-8bfe-3379a9d88797 · outbound

This paper cites Microsoft COCO: Common objects in context.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Microsoft COCO: Common objects in context

Reference 14

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This paper cites Visual instruction tuning.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Visual instruction tuning

Reference 15

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This paper cites GLEAM: En- hanced transferable adversarial attacks for vision-language pre-training models via global-local transformations.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline GLEAM: En- hanced transferable adversarial attacks for vision-language pre-training models via global-local transformations

Reference 16

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This paper cites Frequency domain model augmentation for adversarial attack.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Frequency domain model augmentation for adversarial attack

Reference 17

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Set-level guidance at- tack: Boosting adversarial transferability of vision-language pre-training models

Reference 18

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Observation 69b2f3e0-25a4-459d-836a-c8a9cf69d765 · outbound

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 19

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This paper cites Understanding zero-shot adversarial robust- ness for large-scale models.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Understanding zero-shot adversarial robust- ness for large-scale models

Reference 20

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Unresolved cited work

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This paper cites Plummer, Liwei Wang, Chris M.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Plummer, Liwei Wang, Chris M

Reference 22

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This paper cites Boosting the transferability of ad- versarial attacks with reverse adversarial perturbation.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Boosting the transferability of ad- versarial attacks with reverse adversarial perturbation

Reference 23

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This paper cites Bag of Tricks to Boost Adversarial Transferability.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Bag of Tricks to Boost Adversarial Transferability

Reference 24

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Observation b90aa68c-ee73-42f5-836a-c7ed91aa5f77 · outbound

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Learning transferable visual models from natural language supervi- sion

Reference 25

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Observation 5478d8ac-8c3a-41be-936b-3ac91b08076c · outbound

This paper cites Improving adversarial transferability on vision transformers via forward propagation refinement.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Improving adversarial transferability on vision transformers via forward propagation refinement

Reference 26

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Unresolved cited work

Reference 27

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This paper cites Robust CLIP: Unsupervised adversar- ial fine-tuning of vision embeddings for robust large vision- language models.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Robust CLIP: Unsupervised adversar- ial fine-tuning of vision embeddings for robust large vision- language models

Reference 28

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This paper cites FaceNet: A unified embedding for face recognition and clus- tering.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline FaceNet: A unified embedding for face recognition and clus- tering

Reference 29

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This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 30

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This paper cites A little robustness goes a long way: Leveraging robust features for targeted transfer attacks.Advances in Neural Information Processing Systems, 34:9759–9773, 2021.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline A little robustness goes a long way: Leveraging robust features for targeted transfer attacks.Advances in Neural Information Processing Systems, 34:9759–9773, 2021

Reference 31

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This paper cites Circle loss: A unified perspective of pair similarity optimization.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Circle loss: A unified perspective of pair similarity optimization

Reference 32

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This paper cites Enhancing the transferability of adversarial attacks through variance tuning.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Enhancing the transferability of adversarial attacks through variance tuning

Reference 33

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning

Reference 34

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This paper cites Bovik, H.R.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Bovik, H.R

Reference 35

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Visual Entailment: A Novel Task for Fine-Grained Image Understanding

Reference 36

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability

Reference 37

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Feature squeezing: Detecting adversarial examples in deep neural networks

Reference 38

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Vision-language pre-training with triple contrastive learning

Reference 39

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline VLATTACK: Multimodal adversarial attacks on vision- language tasks via pre-trained models

Reference 40

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This paper cites Modeling context in referring expres- sions.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Modeling context in referring expres- sions

Reference 41

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Towards adversarial attack on vision-language pre-training models

Reference 42

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Univer- sal adversarial perturbations for vision-language pre-trained models

Reference 43

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This paper cites On success and simplicity: A second look at transferable targeted attacks.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline On success and simplicity: A second look at transferable targeted attacks

Reference 44

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This paper cites A unified understanding of adversarial vulnerability regarding unimodal models and vision-language pre-training models.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline A unified understanding of adversarial vulnerability regarding unimodal models and vision-language pre-training models

Reference 45

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

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline AdvCLIP: Downstream-agnostic adversarial examples in multimodal contrastive learning

Reference 46

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Observation f7de823e-eb24-4a9a-9cd3-4d95d13f1193 · outbound

This paper cites Boosting adversarial transferability via gradi- ent relevance attack.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Boosting adversarial transferability via gradi- ent relevance attack

Reference 47

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Unresolved cited work

Reference 2022

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On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline Unresolved cited work

Reference 2025

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