Buchumschlag

Rethinking Model Ensemble in Transfer-based Adversarial Attacks

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Chen, Huanran, Zhang, Yichi, Dong, Yinpeng, Yang, Xiao, Su, Hang, Zhu, Jun
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Computer Vision and Pattern Recognition
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Scaling Laws for Black box Adversarial Attacks
von: Liu, Chuan, et al.
Veröffentlicht: (2024)

Towards Transferable Targeted 3D Adversarial Attack in the Physical World
von: Huang, Yao, et al.
Veröffentlicht: (2023)

Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
von: Zhang, Yichi, et al.
Veröffentlicht: (2024)

Your Diffusion Model is Secretly a Certifiably Robust Classifier
von: Chen, Huanran, et al.
Veröffentlicht: (2024)

Robust Classification via a Single Diffusion Model
von: Chen, Huanran, et al.
Veröffentlicht: (2023)

Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
von: Wu, Lingxuan, et al.
Veröffentlicht: (2024)

BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators
von: Tian, Yu, et al.
Veröffentlicht: (2024)

FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model
von: Chen, Jiawei, et al.
Veröffentlicht: (2024)

Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
von: Wei, Xingxing, et al.
Veröffentlicht: (2024)

Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
von: Zhai, Shengfang, et al.
Veröffentlicht: (2024)

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

MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
von: Zhang, Yichi, et al.
Veröffentlicht: (2024)

Enhancing Adversarial Attacks: The Similar Target Method
von: Zhang, Shuo, et al.
Veröffentlicht: (2023)

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
von: Cheng, Shuyu, et al.
Veröffentlicht: (2024)

DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
von: Kang, Caixin, et al.
Veröffentlicht: (2023)

Benchmarking Transferable Adversarial Attacks
von: Jin, Zhibo, et al.
Veröffentlicht: (2024)

Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective
von: Han, Xiaoxuan, et al.
Veröffentlicht: (2024)

Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
von: Chen, Jun, et al.
Veröffentlicht: (2025)

Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective
von: Tang, Keke, et al.
Veröffentlicht: (2026)

ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
von: Cao, Hanwen, et al.
Veröffentlicht: (2025)

AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
von: Chen, Jiawei, et al.
Veröffentlicht: (2024)

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

Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
von: Ruan, Shouwei, et al.
Veröffentlicht: (2024)

Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
von: Huang, Zhuo, et al.
Veröffentlicht: (2023)

Toward Availability Attacks in 3D Point Clouds
von: Zhu, Yifan, et al.
Veröffentlicht: (2024)

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
von: Yu, Yunrui, et al.
Veröffentlicht: (2025)

Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
von: Cao, Yuxin, et al.
Veröffentlicht: (2024)

Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
von: Fang, Zhengwei, et al.
Veröffentlicht: (2022)

Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability
von: Chen, Junxi, et al.
Veröffentlicht: (2024)

Simulated Ensemble Attack: Transferring Jailbreaks Across Fine-tuned Vision-Language Models
von: Wang, Ruofan, et al.
Veröffentlicht: (2025)

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
von: Yin, Ziyi, et al.
Veröffentlicht: (2024)

SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image Generation
von: Kang, Jingdan, et al.
Veröffentlicht: (2025)

Precise Knowledge Transfer via Flow Matching
von: Shao, Shitong, et al.
Veröffentlicht: (2024)

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization
von: Wang, Jiafeng, et al.
Veröffentlicht: (2022)

Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration
von: Zhou, Fengfan, et al.
Veröffentlicht: (2023)

CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
von: Fang, Hao, et al.
Veröffentlicht: (2024)

AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
von: Ruan, Shouwei, et al.
Veröffentlicht: (2024)

Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
von: Wang, Youze, et al.
Veröffentlicht: (2025)

Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments
von: Yang, Xiao, et al.
Veröffentlicht: (2025)

Adversarial Attack for RGB-Event based Visual Object Tracking
von: Chen, Qiang, et al.
Veröffentlicht: (2025)