Adversarial Machine Learning: Attacks, Defenses, and Open Challenges

TL;DR

This paper provides a comprehensive overview of adversarial machine learning, detailing attack types, defense strategies, and open challenges in creating robust AI systems against malicious manipulations.

Contribution

It offers a formalized analysis of attacks and defenses, and discusses key open issues in deploying secure and scalable AML solutions.

Findings

Detailed taxonomy of AML attacks and defenses

Formal mathematical frameworks for defense mechanisms

Identification of key open challenges in AML deployment

Abstract

Adversarial Machine Learning (AML) addresses vulnerabilities in AI systems where adversaries manipulate inputs or training data to degrade performance. This article provides a comprehensive analysis of evasion and poisoning attacks, formalizes defense mechanisms with mathematical rigor, and discusses the challenges of implementing robust solutions in adaptive threat models. Additionally, it highlights open challenges in certified robustness, scalability, and real-world deployment.