:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Hammoudeh, Zayd, Lowd, Daniel
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2302.11628
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Training Data Influence Analysis and Estimation: A Survey
by: Hammoudeh, Zayd, et al.
Published: (2022)

The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes
by: You, Wencong, et al.
Published: (2025)

Generalization Properties of Adversarial Training for $\ell_0$-Bounded Adversarial Attacks
by: Delgosha, Payam, et al.
Published: (2024)

On the Robustness of Adversarial Training Against Uncertainty Attacks
by: Ledda, Emanuele, et al.
Published: (2024)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024)

Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Tight Robustness Certification Through the Convex Hull of $\ell_0$ Attacks
by: Shapira, Yuval, et al.
Published: (2025)

MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants
by: Heibel, John, et al.
Published: (2024)

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

Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
by: Askarizadeh, Mohammad Javad, et al.
Published: (2024)

Deep learning in medical image registration: introduction and survey
by: Hammoudeh, Ahmad, et al.
Published: (2023)

Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning
by: Luo, Zhi, et al.
Published: (2024)

Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
by: Giri, Nandakrishna, et al.
Published: (2026)

Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examples
by: Lukyanov, Kirill, et al.
Published: (2024)

Exploring Sparsity and Smoothness of Arbitrary $\ell_p$ Norms in Adversarial Attacks
by: Duhme, Christof, et al.
Published: (2026)

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
by: Liu, Xiangyu, et al.
Published: (2023)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)

Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks
by: Pasque, Kurt, et al.
Published: (2024)

Stochastic Bandits Robust to Adversarial Attacks
by: Wang, Xuchuang, et al.
Published: (2024)

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
by: Cui, Canyixing, et al.
Published: (2026)

Sampling-aware Adversarial Attacks Against Large Language Models
by: Beyer, Tim, et al.
Published: (2025)

Adversarial Training for Defense Against Label Poisoning Attacks
by: Bal, Melis Ilayda, et al.
Published: (2025)

DARD: Dice Adversarial Robustness Distillation against Adversarial Attacks
by: Zou, Jing, et al.
Published: (2025)

Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
by: Hong, Hanbin, et al.
Published: (2023)

Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging
by: Luo, Rui, et al.
Published: (2024)

Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
by: Chen, Jinyin, et al.
Published: (2024)

Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
by: Tecot, Lucas, et al.
Published: (2025)

Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
by: Brown, Hannah, et al.
Published: (2024)

Deep Adversarial Defense Against Multilevel-Lp Attacks
by: Wang, Ren, et al.
Published: (2024)

Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
by: Luo, Jiayi, et al.
Published: (2025)

A White-Box Adversarial Attack Against a Digital Twin
by: Patterson, Wilson, et al.
Published: (2022)

Provably Invincible Adversarial Attacks on Reinforcement Learning Systems: A Rate-Distortion Information-Theoretic Approach
by: Lu, Ziqing, et al.
Published: (2025)

Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
by: Schuchardt, Jan, et al.
Published: (2023)

Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
by: Simonetto, Thibault, et al.
Published: (2024)

TA3: Testing Against Adversarial Attacks on Machine Learning Models
by: Jin, Yuanzhe, et al.
Published: (2024)

Detecting Adversarial Data via Provable Adversarial Noise Amplification
by: Mumcu, Furkan, et al.
Published: (2026)

Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks
by: Xu, Yinglun, et al.
Published: (2024)

Provable Watermarking for Data Poisoning Attacks
by: Zhu, Yifan, et al.
Published: (2025)

How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)

Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
by: Saxena, Aman, et al.
Published: (2026)