Cover Image

Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lv, Kangtao, Cao, Huangsen, Tu, Kainan, Xu, Yihuai, Zhang, Zhimeng, Ding, Xin, Wang, Yongwei
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs
by: Cao, Huangsen, et al.
Published: (2024)

ASAM: Boosting Segment Anything Model with Adversarial Tuning
by: Li, Bo, et al.
Published: (2024)

Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models
by: Xing, Songlong, et al.
Published: (2026)

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
by: Guan, Jiwei, et al.
Published: (2024)

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

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks
by: Ding, Xin, et al.
Published: (2023)

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

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

On the Adversarial Robustness of 3D Large Vision-Language Models
by: Liu, Chao, et al.
Published: (2026)

A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models
by: Mirza, Mujtaba Hussain, et al.
Published: (2026)

REVEAL: Reasoning-Enhanced Forensic Evidence Analysis for Explainable AI-Generated Image Detection
by: Cao, Huangsen, et al.
Published: (2025)

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

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

Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
by: Park, Dongmin, et al.
Published: (2024)

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

Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models
by: Cheng, Hao, et al.
Published: (2024)

Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models
by: Liu, Xiao, et al.
Published: (2026)

FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers
by: Yuan, Zheng, 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)

Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
by: Wang, Zeyu, et al.
Published: (2025)

Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
by: Wang, Sibo, et al.
Published: (2024)

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

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

A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models
by: Liang, Yuhan, et al.
Published: (2024)

Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector
by: Cao, Yiming, et al.
Published: (2025)

Boosting Adversarial Transferability with Spatial Adversarial Alignment
by: Chen, Zhaoyu, et al.
Published: (2025)

Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language Models
by: Jia, Xiaojun, et al.
Published: (2025)

Survey of Adversarial Robustness in Multimodal Large Language Models
by: Jiang, Chengze, et al.
Published: (2025)

Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models
by: Liu, Jiaxiang, et al.
Published: (2025)

Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
by: Kumano, Soichiro, et al.
Published: (2025)

Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration
by: Liao, Chih-Ting, et al.
Published: (2025)

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

Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation
by: Budathoki, Anjila, et al.
Published: (2025)

Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology
by: Malik, Hashmat Shadab, et al.
Published: (2025)

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models
by: Li, Zhiwei, et al.
Published: (2026)

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

ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection
by: Cao, Huangsen, et al.
Published: (2026)

On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
by: Zhang, Xinwei, et al.
Published: (2026)

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

Adversarial Robustness of Discriminative Self-Supervised Learning in Vision
by: Çağatan, Ömer Veysel, et al.
Published: (2025)