Cover Image

Position: Towards Resilience Against Adversarial Examples

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dai, Sihui, Xiang, Chong, Wu, Tong, Mittal, Prateek
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
by: Jacob, Dennis, et al.
Published: (2025)

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
by: Xiang, Chong, et al.
Published: (2023)

Certifiably Robust RAG against Retrieval Corruption
by: Xiang, Chong, et al.
Published: (2024)

Improved Generation of Adversarial Examples Against Safety-aligned LLMs
by: Li, Qizhang, et al.
Published: (2024)

Detecting Adversarial Examples
by: Mumcu, Furkan, et al.
Published: (2024)

Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
by: Echeberria-Barrio, Xabier, et al.
Published: (2024)

Transferability Ranking of Adversarial Examples
by: Levy, Mosh, et al.
Published: (2022)

ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
by: Kocal, Cagla Ipek, et al.
Published: (2025)

Laundering AI Authority with Adversarial Examples
by: Zhang, Jie, et al.
Published: (2026)

SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge
by: Fenaux, Lucas, et al.
Published: (2024)

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

Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective
by: Zhang, Andi, et al.
Published: (2023)

When and How to Fool Explainable Models (and Humans) with Adversarial Examples
by: Vadillo, Jon, et al.
Published: (2021)

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

Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies
by: Li, Li
Published: (2024)

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

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
by: Longari, Stefano, et al.
Published: (2025)

Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
by: Thakkar, Janvi, et al.
Published: (2023)

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

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
by: Schneider, Thomas, et al.
Published: (2024)

Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy
by: Wu, Tong, et al.
Published: (2024)

Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
by: Yao, Likai, et al.
Published: (2024)

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

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

Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels
by: Shukla, Shubhi, et al.
Published: (2024)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
by: Yao, Duanyi, et al.
Published: (2024)

The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
by: Hao, Yifan, et al.
Published: (2024)

Private Fine-tuning of Large Language Models with Zeroth-order Optimization
by: Tang, Xinyu, et al.
Published: (2024)

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
by: Panda, Ashwinee, et al.
Published: (2022)

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation
by: Yang, Kun, et al.
Published: (2025)

Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
by: Sahabandu, Dinuka, et al.
Published: (2024)

CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications
by: Zhang, Hangsheng, et al.
Published: (2024)

Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
by: Ma, Jie, et al.
Published: (2025)

PEAS: A Strategy for Crafting Transferable Adversarial Examples
by: Avraham, Bar, et al.
Published: (2024)

Rectifying Adversarial Examples Using Their Vulnerabilities
by: Morimoto, Fumiya, et al.
Published: (2026)

RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness
by: Jiang, Enyi, et al.
Published: (2024)

Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
by: Zizzo, Giulio, et al.
Published: (2025)

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

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

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026)