Adversarial Machine Learning: Threats and Mitigation Strategies

Abstract

Adversarial machine learning is an emerging field that focuses on the
vulnerabilities of machine learning models to adversarial attacks—intentional manipulations of
input data designed to deceive models into making incorrect predictions. This paper reviews the
state of adversarial machine learning before 2013, discussing the different types of threats (e.g.,
evasion attacks, poisoning attacks), potential impacts on machine learning systems, and
strategies for mitigating these threats. The paper explores early techniques such as adversarial
training, model regularization, and anomaly detection, highlighting their strengths and
limitations. While significant challenges remain, early research laid the groundwork for future
advancements in building more robust machine learning systems.

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