Robust Adversarial Training

Robust adversarial training aims to improve the resilience of machine learning models, particularly deep neural networks, against adversarial attacks—malicious inputs designed to fool the model. Current research focuses on mitigating vulnerabilities arising from data corruption, label noise, and model updates that negatively impact robustness, employing techniques like hybrid adversarial training, constrained fine-tuning, and hierarchical regularization within various architectures including graph neural networks. These advancements are crucial for enhancing the reliability and security of machine learning systems across diverse applications, from computer vision to graph-based data analysis, where model robustness is paramount.

Papers

May 7, 2024

Effective and Robust Adversarial Training against Data and Label Corruptions
Peng-Fei Zhang, Zi Huang, Xin-Shun Xu, Guangdong Bai
Adversarial Training Raw Data Adversarial Perturbation Data Perturbation Label Corruption Robust Adversarial Training

February 27, 2024

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto, Davide Anguita, Battista Biggio, Fabio Roli
Adversarial Example Adversarial Training Adversarial Robustness Secure Machine Learning Robust Adversarial Training

December 8, 2023

HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs
Xiaobing Pei, Haoran Yang, Gang Shen
Adversarial Attack Adversarial Example Adversarial Training Gene Level GNN Iterative Refinement Graph Perturbation Robust Adversarial Training

May 30, 2022

Robust Weight Perturbation for Adversarial Training
Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, Tongliang Liu
Adversarial Training Weight Perturbation Adversarial Weight Perturbation Robust Adversarial Training

March 26, 2022

A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
Zhuang Qian, Kaizhu Huang, Qiu-Feng Wang, Xu-Yao Zhang
Timely Survey Adversarial Attack Adversarial Example Adversarial Training NCD Method Theoretical Understanding Adversarial Sample Pattern Recognition Robust Adversarial Training

February 18, 2022

Exploring Adversarially Robust Training for Unsupervised Domain Adaptation
Shao-Yuan Lo, Vishal M. Patel
Adversarial Attack Domain Adaptation Adversarial Training Adversarial Robustness Robust Training Robust Adversarial Training

November 3, 2021

LTD: Low Temperature Distillation for Robust Adversarial Training
Erh-Chung Chen, Che-Rung Lee
Adversarial Attack Adversarial Training Low Temperature Distillation Gradient Masking Defensive Distillation Robust Adversarial Training