Imagen de Portada

Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Warnecke, Alexander, Rieck, Konrad
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

On the Detection of Image-Scaling Attacks in Machine Learning
por: Quiring, Erwin, et al.
Publicado: (2023)

JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example
por: Tondi, Benedetta, et al.
Publicado: (2024)

Manipulating Feature Visualizations with Gradient Slingshots
por: Bareeva, Dilyara, et al.
Publicado: (2024)

Adversarial Examples Are Not Real Features
por: Li, Ang, et al.
Publicado: (2023)

Adversarial Examples Detection with Bayesian Neural Network
por: Li, Yao, et al.
Publicado: (2021)

Do Counterfactual Examples Complicate Adversarial Training?
por: Yeats, Eric, et al.
Publicado: (2024)

Adversarial Example Defense via Perturbation Grading Strategy
por: Zhu, Shaowei, et al.
Publicado: (2022)

Point Cloud Synthesis Using Inner Product Transforms
por: Röell, Ernst, et al.
Publicado: (2024)

Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
por: Gui, Jie, et al.
Publicado: (2024)

Large Convolutional Model Tuning via Filter Subspace
por: Chen, Wei, et al.
Publicado: (2024)

Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
por: Kamal, Khaleque Md Aashiq, et al.
Publicado: (2026)

Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities
por: Wu, Jialin, et al.
Publicado: (2025)

Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
por: Haque, Nashrah, et al.
Publicado: (2024)

AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models
por: Dai, Xuelong, et al.
Publicado: (2023)

Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
por: Gubri, Martin, et al.
Publicado: (2023)

DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
por: Nafi, Abdullah Al Nomaan, et al.
Publicado: (2025)

Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations
por: Zhang, Haimin, et al.
Publicado: (2021)

Transferable Adversarial Examples with Bayes Approach
por: Fan, Mingyuan, et al.
Publicado: (2022)

Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
por: Cai, Xu, et al.
Publicado: (2025)

Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
por: Xu, Cong, et al.
Publicado: (2022)

Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics
por: Li, Shuai, et al.
Publicado: (2024)

DRIFT: Divergent Response in Filtered Transformations for Robust Adversarial Defense
por: Guesmi, Amira, et al.
Publicado: (2025)

Adversarial Examples in the Physical World: A Survey
por: Wang, Jiakai, et al.
Publicado: (2023)

(Almost) Free Modality Stitching of Foundation Models
por: Singh, Jaisidh, et al.
Publicado: (2025)

SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models
por: Dai, Zeyu, et al.
Publicado: (2025)

Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation
por: Wu, Wenyuan, et al.
Publicado: (2025)

Enhancing Adversarial Example Detection Through Model Explanation
por: Ma, Qian, et al.
Publicado: (2025)

Improving Transferability of Adversarial Examples via Bayesian Attacks
por: Li, Qizhang, et al.
Publicado: (2023)

Dendritic Convolution for Noise Image Recognition
por: Xue, Jiarui, et al.
Publicado: (2025)

Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples
por: Irshad, Samra, et al.
Publicado: (2025)

Adversarial Examples: Generation Proposal in the Context of Facial Recognition Systems
por: Fuster, Marina, et al.
Publicado: (2024)

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
por: Cinà, Antonio Emanuele, et al.
Publicado: (2024)

AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation
por: Peng, Heqi, et al.
Publicado: (2024)

Ungeneralizable Examples
por: Ye, Jingwen, et al.
Publicado: (2024)

Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples
por: Yilmaz, Eda, et al.
Publicado: (2024)

Image Restoration Using Deep Regulated Convolutional Networks
por: Liu, Peng, et al.
Publicado: (2019)

RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
por: Zhang, Haimin, et al.
Publicado: (2024)

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation
por: Yu, Yi, et al.
Publicado: (2025)

Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense
por: Zhang, Haibo, et al.
Publicado: (2025)

Revisiting Transformers with Insights from Image Filtering and Boosting
por: Abdullaev, Laziz U., et al.
Publicado: (2025)