# Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks

## TL;DR

This paper evaluates the robustness of three generalized Learning Vector Quantization models against adversarial attacks, finding that some models exhibit high robustness comparable to state-of-the-art neural networks, while others are more vulnerable.

## Contribution

It provides the first extensive comparison of LVQ models' robustness to adversarial attacks, highlighting the impact of prototype quantity on model resilience.

## Key findings

- Generalized LVQ and Generalized Tangent LVQ show high robustness.
- Generalized Matrix LVQ is highly susceptible to attacks.
- Increasing prototypes per class enhances robustness.

## Abstract

Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.