Copertina

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Guan, Jiwei, Ding, Tianyu, Cao, Longbing, Pan, Lei, Wang, Chen, Zheng, Xi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Adversarial Prompt Injection Attack on Multimodal Large Language Models
di: Ding, Meiwen, et al.
Pubblicazione: (2026)

Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
di: Guan, Jiwei, et al.
Pubblicazione: (2026)

When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models
di: Yan, Yuping, et al.
Pubblicazione: (2025)

Attention-Guided Patch-Wise Sparse Adversarial Attacks on Vision-Language-Action Models
di: Zhang, Naifu, et al.
Pubblicazione: (2025)

Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models
di: Lv, Kangtao, et al.
Pubblicazione: (2024)

Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks
di: Hossain, Md Zarif, et al.
Pubblicazione: (2024)

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving
di: Wang, Lu, et al.
Pubblicazione: (2025)

AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models
di: Li, Jiayu, et al.
Pubblicazione: (2025)

SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion
di: Pan, Zihao, et al.
Pubblicazione: (2024)

Adversarial Defense in Vision-Language Models: An Overview
di: Fu, Xiaowei, et al.
Pubblicazione: (2026)

Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
di: Patel, Het, et al.
Pubblicazione: (2025)

Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
di: Zheng, Weijie, et al.
Pubblicazione: (2024)

A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models
di: Chen, Wutao, et al.
Pubblicazione: (2026)

Enhancing Adversarial Robustness of Vision-Language Models through Low-Rank Adaptation
di: Ji, Yuheng, et al.
Pubblicazione: (2024)

Robust SAM: On the Adversarial Robustness of Vision Foundation Models
di: Long, Jiahuan, et al.
Pubblicazione: (2025)

Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks
di: Xie, Peng, et al.
Pubblicazione: (2024)

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
di: Yin, Ziyi, et al.
Pubblicazione: (2023)

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models
di: Li, Lin, et al.
Pubblicazione: (2024)

Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
di: Chung-En, et al.
Pubblicazione: (2025)

PIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe Questions
di: Zhang, Yudong, et al.
Pubblicazione: (2024)

Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
di: Zhou, Wanqi, et al.
Pubblicazione: (2024)

Nested AutoRegressive Models
di: Wu, Hongyu, et al.
Pubblicazione: (2025)

AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models
di: Cui, Yubo, et al.
Pubblicazione: (2026)

HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
di: Liu, Han, et al.
Pubblicazione: (2026)

Tex3D: Objects as Attack Surfaces via Adversarial 3D Textures for Vision-Language-Action Models
di: Chen, Jiawei, et al.
Pubblicazione: (2026)

Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models
di: Wang, Yubo, et al.
Pubblicazione: (2024)

Robustness of Vision Language Models Against Split-Image Harmful Input Attacks
di: Rashid, Md Rafi Ur, et al.
Pubblicazione: (2026)

B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions
di: Zhang, Hao, et al.
Pubblicazione: (2024)

Explainable Adversarial-Robust Vision-Language-Action Model for Robotic Manipulation
di: Kim, Ju-Young, et al.
Pubblicazione: (2025)

NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
di: Zhang, Jiaming, et al.
Pubblicazione: (2025)

AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization
di: Liu, Chaohu, et al.
Pubblicazione: (2025)

TTP: Test-Time Padding for Adversarial Detection and Robust Adaptation on Vision-Language Models
di: Li, Zhiwei, et al.
Pubblicazione: (2025)

Do Language Models Understand Time?
di: Ding, Xi, et al.
Pubblicazione: (2024)

LMFusion: Adapting Pretrained Language Models for Multimodal Generation
di: Shi, Weijia, et al.
Pubblicazione: (2024)

Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks
di: Chen, Erh-Chung, et al.
Pubblicazione: (2024)

Language-Guided Invariance Probing of Vision-Language Models
di: Lee, Jae Joong
Pubblicazione: (2025)

Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
di: Schlarmann, Christian, et al.
Pubblicazione: (2024)

CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
di: Kumar, Ashwin, et al.
Pubblicazione: (2026)

Adversarial Prompt Tuning for Vision-Language Models
di: Zhang, Jiaming, et al.
Pubblicazione: (2023)

Adversarial Prompt Distillation for Vision-Language Models
di: Luo, Lin, et al.
Pubblicazione: (2024)