Hardening ML Classifiers. A Brief Review

Machine learning (ML) classifiers are a fundamental component of ML and are widely used in a variety of applications, including image and speech recognition, natural language processing, and bioinformatics. They are models that are trained to make predictions about the class or category of an input data point. However, classifiers are also subject to adversarial attacks, which can cause misclassifications and potentially lead to abuse. In this article, we will discuss the various ways in which classifiers can be exploited, and methods that can be used to harden classifiers against these attacks. Adversarial attacks on classifiers involve manipulating the input data, such as images or speech, in order to cause the classifier to make a misclassification. These attacks can be performed by adding small, carefully crafted perturbations to the input data, called adversarial examples, that are designed to confuse the classifier.