:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yeats, Eric, Darwin, Cameron, Ortega, Eduardo, Liu, Frank, Li, Hai
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2404.10588
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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