To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
Fuente:
arXiv
Saved in:
| Main Authors: | Zhao, Chengshuai, Tan, Zhen, Li, Dawei, Yu, Zhiyuan, Liu, Huan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025)
by: Ding, Yi, et al.
Published: (2025)
Anti-Tamper Protection for Unauthorized Individual Image Generation
by: Li, Zelin, et al.
Published: (2025)
by: Li, Zelin, et al.
Published: (2025)
Defending Unauthorized Model Merging via Dual-Stage Weight Protection
by: Chen, Wei-Jia, et al.
Published: (2025)
by: Chen, Wei-Jia, et al.
Published: (2025)
Image-Based Geolocation Using Large Vision-Language Models
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models
by: Chen, Ruoxi, et al.
Published: (2023)
by: Chen, Ruoxi, et al.
Published: (2023)
Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study
by: Lee, DongGeon, et al.
Published: (2025)
by: Lee, DongGeon, et al.
Published: (2025)
FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
by: Cui, Yingqian, et al.
Published: (2023)
by: Cui, Yingqian, et al.
Published: (2023)
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation
by: Zhong, Zhiyuan, et al.
Published: (2025)
by: Zhong, Zhiyuan, et al.
Published: (2025)
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
by: Wu, Qiangqiang, et al.
Published: (2025)
by: Wu, Qiangqiang, et al.
Published: (2025)
Recovering the Pre-Fine-Tuning Weights of Generative Models
by: Horwitz, Eliahu, et al.
Published: (2024)
by: Horwitz, Eliahu, et al.
Published: (2024)
Jailbreaking Attack against Multimodal Large Language Model
by: Niu, Zhenxing, et al.
Published: (2024)
by: Niu, Zhenxing, et al.
Published: (2024)
Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services
by: Li, Yansong, et al.
Published: (2023)
by: Li, Yansong, et al.
Published: (2023)
Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector
by: Huang, Youcheng, et al.
Published: (2024)
by: Huang, Youcheng, et al.
Published: (2024)
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
by: Jin, Haibo, et al.
Published: (2024)
by: Jin, Haibo, et al.
Published: (2024)
Revisiting Data Auditing in Large Vision-Language Models
by: Zhu, Hongyu, et al.
Published: (2025)
by: Zhu, Hongyu, et al.
Published: (2025)
Doubly-Universal Adversarial Perturbations: Deceiving Vision-Language Models Across Both Images and Text with a Single Perturbation
by: Kim, Hee-Seon, et al.
Published: (2024)
by: Kim, Hee-Seon, et al.
Published: (2024)
Few-Shot Adversarial Prompt Learning on Vision-Language Models
by: Zhou, Yiwei, et al.
Published: (2024)
by: Zhou, Yiwei, et al.
Published: (2024)
InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
by: Sun, Mengyuan, et al.
Published: (2025)
by: Sun, Mengyuan, et al.
Published: (2025)
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications
by: Trad, Fouad, et al.
Published: (2024)
by: Trad, Fouad, et al.
Published: (2024)
Test-Time Backdoor Attacks on Multimodal Large Language Models
by: Lu, Dong, et al.
Published: (2024)
by: Lu, Dong, et al.
Published: (2024)
Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory
by: Zhang, Ce, et al.
Published: (2026)
by: Zhang, Ce, et al.
Published: (2026)
Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
by: Schlegel, Viktor, et al.
Published: (2025)
by: Schlegel, Viktor, et al.
Published: (2025)
Iteratively Prompting Multimodal LLMs to Reproduce Natural and AI-Generated Images
by: Naseh, Ali, et al.
Published: (2024)
by: Naseh, Ali, et al.
Published: (2024)
Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
by: Qu, Jingen, et al.
Published: (2025)
by: Qu, Jingen, et al.
Published: (2025)
Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities
by: Xiong, Yuan, et al.
Published: (2025)
by: Xiong, Yuan, et al.
Published: (2025)
SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts
by: Wu, Xiaodong, et al.
Published: (2025)
by: Wu, Xiaodong, et al.
Published: (2025)
DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models
by: Wang, Zhenting, et al.
Published: (2023)
by: Wang, Zhenting, et al.
Published: (2023)
SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
by: Li, Junxian, et al.
Published: (2026)
by: Li, Junxian, et al.
Published: (2026)
Differentially Private Bias-Term Fine-tuning of Foundation Models
by: Bu, Zhiqi, et al.
Published: (2022)
by: Bu, Zhiqi, et al.
Published: (2022)
VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models
by: Liao, Qilin, et al.
Published: (2025)
by: Liao, Qilin, et al.
Published: (2025)
Membership Inference Attacks against Large Vision-Language Models
by: Li, Zhan, et al.
Published: (2024)
by: Li, Zhan, et al.
Published: (2024)
A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
by: Ye, Mang, et al.
Published: (2025)
by: Ye, Mang, et al.
Published: (2025)
Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security
by: Fan, Yihe, et al.
Published: (2024)
by: Fan, Yihe, et al.
Published: (2024)
Dissecting Adversarial Robustness of Multimodal LM Agents
by: Wu, Chen Henry, et al.
Published: (2024)
by: Wu, Chen Henry, et al.
Published: (2024)
Backdoor Mitigation in Object Detection via Adversarial Fine-Tuning
by: Dunnett, Kealan, et al.
Published: (2026)
by: Dunnett, Kealan, et al.
Published: (2026)
Benchmarking Large Multimodal Models against Common Corruptions
by: Zhang, Jiawei, et al.
Published: (2024)
by: Zhang, Jiawei, et al.
Published: (2024)
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
by: Afane, Mohamed, et al.
Published: (2025)
by: Afane, Mohamed, et al.
Published: (2025)
Privacy-Preserving Federated Learning with Verifiable Fairness Guarantees
by: Ali, Mohammed Himayath, et al.
Published: (2026)
by: Ali, Mohammed Himayath, et al.
Published: (2026)
From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data
by: Akcay, Mete Harun, et al.
Published: (2025)
by: Akcay, Mete Harun, et al.
Published: (2025)
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
by: Yin, Ziyi, et al.
Published: (2023)
by: Yin, Ziyi, et al.
Published: (2023)
Similar Items
-
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025) -
Anti-Tamper Protection for Unauthorized Individual Image Generation
by: Li, Zelin, et al.
Published: (2025) -
Defending Unauthorized Model Merging via Dual-Stage Weight Protection
by: Chen, Wei-Jia, et al.
Published: (2025) -
Image-Based Geolocation Using Large Vision-Language Models
by: Liu, Yi, et al.
Published: (2024) -
EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models
by: Chen, Ruoxi, et al.
Published: (2023)