Imagen de Portada

Adversarial Guided Diffusion Models for Adversarial Purification

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lin, Guang, Tao, Zerui, Zhang, Jianhai, Tanaka, Toshihisa, Zhao, Qibin
Formato:	Preprint
Publicado:	2024
Materias:	Computer Vision and Pattern Recognition Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
por: Lin, Guang, et al.
Publicado: (2024)

Large Language Model Sentinel: LLM Agent for Adversarial Purification
por: Lin, Guang, et al.
Publicado: (2024)

Random Sampling for Diffusion-based Adversarial Purification
por: Zhang, Jiancheng, et al.
Publicado: (2024)

DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models
por: Fu, Jia, et al.
Publicado: (2025)

Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification
por: Pei, Gaozheng, et al.
Publicado: (2025)

Instant Adversarial Purification with Adversarial Consistency Distillation
por: Lei, Chun Tong, et al.
Publicado: (2024)

Structure-Guided Adversarial Training of Diffusion Models
por: Yang, Ling, et al.
Publicado: (2024)

Efficient Model-Based Purification Against Adversarial Attacks for LiDAR Segmentation
por: Gkillas, Alexandros, et al.
Publicado: (2025)

Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
por: Huang, Tao, et al.
Publicado: (2024)

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

CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
por: Zhang, Mingkun, et al.
Publicado: (2025)

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

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

Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
por: Wan, Cong, et al.
Publicado: (2024)

Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
por: Zhao, Shiji, et al.
Publicado: (2026)

Fighting Fire with Fire (F3): A Training-free and Efficient Visual Adversarial Example Purification Method in LVLMs
por: Zhang, Yudong, et al.
Publicado: (2025)

Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
por: Zhu, Peifei, et al.
Publicado: (2024)

TAIGen: Training-Free Adversarial Image Generation via Diffusion Models
por: Roy, Susim, et al.
Publicado: (2025)

SDXL-Lightning: Progressive Adversarial Diffusion Distillation
por: Lin, Shanchuan, et al.
Publicado: (2024)

Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model
por: Liu, Haixu, et al.
Publicado: (2025)

PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks
por: Guo, Ping, et al.
Publicado: (2024)

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

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

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

AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
por: Zeng, Yaopei, et al.
Publicado: (2024)

Adversarial Error Correction for Visual Autoregressive Generation
por: Bi, Ligong, et al.
Publicado: (2026)

Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
por: Li, Binghua, et al.
Publicado: (2025)

Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
por: Li, Yumeng, et al.
Publicado: (2024)

SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation
por: Wang, Zixi, et al.
Publicado: (2025)

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

DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
por: Sun, Yuhao, et al.
Publicado: (2024)

Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation
por: Yu, Jongmin, et al.
Publicado: (2024)

Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study
por: Ye, Kai, et al.
Publicado: (2025)

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
por: Xue, Haotian, et al.
Publicado: (2023)

Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think
por: Xue, Haotian, et al.
Publicado: (2024)

Paired Image Generation with Diffusion-Guided Diffusion Models
por: Zhang, Haoxuan, et al.
Publicado: (2025)

Adversarial Camouflage
por: Borsukiewicz, Paweł, et al.
Publicado: (2026)

Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey
por: Zhang, Chenyu, et al.
Publicado: (2024)

Adversarial Attack Against Images Classification based on Generative Adversarial Networks
por: Yang, Yahe
Publicado: (2024)

STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-based Video Models
por: Wang, Zerui, et al.
Publicado: (2024)