Imagen de Portada

Do Counterfactual Examples Complicate Adversarial Training?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yeats, Eric, Darwin, Cameron, Ortega, Eduardo, Liu, Frank, Li, Hai
Formato:	Preprint
Publicado:	2024
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

Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
por: Gui, Jie, 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)

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

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

Saddle-Free Guidance: Improved On-Manifold Sampling without Labels or Additional Training
por: Yeats, Eric, et al.
Publicado: (2025)

Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
por: Warnecke, Alexander, et al.
Publicado: (2026)

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

Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
por: Goldwasser, Jeremy, et al.
Publicado: (2025)

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

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

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)

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)

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

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

Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
por: Liu, Zhenyu, et al.
Publicado: (2024)

Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation
por: Zhang, Lilin, et al.
Publicado: (2026)

MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
por: Hu, Zhaozhe, et al.
Publicado: (2024)

Contrastive Adversarial Training for Unsupervised Domain Adaptation
por: Chen, Jiahong, et al.
Publicado: (2024)

Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation
por: Mi, Xiaoyue, et al.
Publicado: (2023)

TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
por: Medi, Tejaswini, et al.
Publicado: (2025)

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

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
por: Xie, Ming-Kun, et al.
Publicado: (2024)

Nested Annealed Training Scheme for Generative Adversarial Networks
por: Wan, Chang, et al.
Publicado: (2025)

Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
por: Dong, Junhao, et al.
Publicado: (2024)

What is Adversarial Training for Diffusion Models?
por: Rosaria, Briglia Maria, et al.
Publicado: (2025)

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

Towards Adversarially Robust Dataset Distillation by Curvature Regularization
por: Xue, Eric, et al.
Publicado: (2024)

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
por: Zhang, Jiacheng, et al.
Publicado: (2024)

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

Adversarial Training from Mean Field Perspective
por: Kumano, Soichiro, et al.
Publicado: (2025)

Compositional Adversarial Training for Robust Visual Watermarking
por: Satheesh, Anirudh, et al.
Publicado: (2026)

Understanding Adversarial Training with Energy-based Models
por: Mirza, Mujtaba Hussain, et al.
Publicado: (2025)

Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path
por: Ren, Yuchen, et al.
Publicado: (2024)

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