# Robustness Verification of Support Vector Machines

## TL;DR

This paper introduces a formal, abstract verification method for assessing the robustness of support vector machines against adversarial examples, demonstrating efficiency and high provable robustness on standard image datasets.

## Contribution

It presents a novel, sound abstraction-based approach for verifying SVM robustness without requiring complex domain widenings, applicable to various kernel types.

## Key findings

- Verification is fast and scalable.
- High percentages of provable robustness on MNIST.
- Effective on both linear and nonlinear kernels.

## Abstract

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks.