Cover Image

NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Jiaming, Wang, Xin, Ma, Xingjun, Qiu, Lingyu, Jiang, Yu-Gang, Sang, Jitao
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
by: Wang, Xin, et al.
Published: (2024)

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models
by: Zhai, Kun, et al.
Published: (2025)

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

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
by: Wang, Xin, et al.
Published: (2026)

Debiased Prompt Tuning in Vision-Language Model without Annotations
by: Jiang, Chaoquan, et al.
Published: (2025)

AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
by: Wang, Xin, et al.
Published: (2024)

Revisiting the Robust Generalization of Adversarial Prompt Tuning
by: Yang, Fan, et al.
Published: (2024)

Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
by: Hu, Rui, et al.
Published: (2024)

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

Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
by: Zhang, Zhifang, et al.
Published: (2024)

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

Prompt Tuning with Soft Context Sharing for Vision-Language Models
by: Ding, Kun, et al.
Published: (2022)

Language-assisted Vision Model Debugger: A Sample-Free Approach to Finding and Fixing Bugs
by: Jiang, Chaoquan, et al.
Published: (2023)

White-box Multimodal Jailbreaks Against Large Vision-Language Models
by: Wang, Ruofan, et al.
Published: (2024)

LPT: Less-overfitting Prompt Tuning for Vision-Language Model
by: Ding, Chenhao, et al.
Published: (2024)

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

ADT: Tuning Diffusion Models with Adversarial Supervision
by: Shen, Dazhong, et al.
Published: (2025)

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
by: Sun, Ye, et al.
Published: (2026)

Remodeling Semantic Relationships in Vision-Language Fine-Tuning
by: Wu, Xiangyang, et al.
Published: (2025)

Towards Calibrated Robust Fine-Tuning of Vision-Language Models
by: Oh, Changdae, et al.
Published: (2023)

MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
by: Zhao, Shiji, et al.
Published: (2025)

Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language Models
by: Peng, Wei, et al.
Published: (2025)

Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
by: Li, Haoyang, et al.
Published: (2025)

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

BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
by: Zhao, Yunhan, et al.
Published: (2024)

MINT: Memory-Infused Prompt Tuning at Test-time for CLIP
by: Yi, Jiaming, et al.
Published: (2025)

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

Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
by: Lan, Long, et al.
Published: (2024)

BadPatch: Diffusion-Based Generation of Physical Adversarial Patches
by: Wang, Zhixiang, et al.
Published: (2024)

IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning
by: Ghosal, Soumya Suvra, et al.
Published: (2024)

CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment
by: Shao, Maoyuan, et al.
Published: (2026)

IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
by: Wang, Ruofan, et al.
Published: (2024)

Robust Prompt Tuning for Vision-Language Models with Mild Semantic Noise
by: Gao, Yansheng, et al.
Published: (2025)

Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models
by: Song, Fei, et al.
Published: (2025)

Defense-to-Attack: Bypassing Weak Defenses Enables Stronger Jailbreaks in Vision-Language Models
by: Zhao, Yunhan, et al.
Published: (2025)

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

Embedded Visual Prompt Tuning
by: Zu, Wenqiang, et al.
Published: (2024)

Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images
by: Stein, Kyle, et al.
Published: (2024)

Generalizable Prompt Tuning for Vision-Language Models
by: Zhang, Qian
Published: (2024)