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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Asymptotic Behavior of Adversarial Training in Binary Linear Classification.

Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis

IEEE Transactions on Neural Networks and Learning Systems

|July 18, 2023

Summary

Adversarial training enhances classification robustness against attacks. This study precisely characterizes adversarial training

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Area of Science:

Machine Learning
Computer Vision
Robustness in AI

Background:

Adversarial training with empirical risk minimization (ERM) is a leading defense against adversarial attacks.
Understanding the generalization properties of adversarial training remains a challenge.
This study focuses on binary linear classification in a high-dimensional setting.

Purpose of the Study:

To precisely characterize the robustness of adversarial training in binary linear classification.
To analyze generalization properties under adversarial perturbations.
To investigate the fundamental limits of adversarial training.

Main Methods:

Analysis in the high-dimensional regime where model dimension scales with training set size.
Derivation of exact asymptotic error formulas for standard and adversarial test errors.
Consideration of general p-norm bounded perturbations in discriminative and generative models.

Main Results:

Exact asymptotic formulas for test errors under adversarial perturbations.
Explanation of how over-parameterization, data models, and attack budgets influence errors.
Comparison with robust Bayes estimators to understand fundamental limits.

Conclusions:

Provides a precise characterization of adversarial training robustness in binary linear classification.
Offers insights into the impact of key factors on adversarial and standard errors.
Establishes a framework for studying the theoretical limits of adversarial training defenses.