Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks
Machine Learning
2022-09-29 v1 Artificial Intelligence
Abstract
We propose using a two-layered deployment of machine learning models to prevent adversarial attacks. The first layer determines whether the data was tampered, while the second layer solves a domain-specific problem. We explore three sets of features and three dataset variations to train machine learning models. Our results show clustering algorithms achieved promising results. In particular, we consider the best results were obtained by applying the DBSCAN algorithm to the structured structural similarity index measure computed between the images and a white reference image.
Cite
@article{arxiv.2209.13963,
title = {Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks},
author = {Jože M. Rožanec and Dimitrios Papamartzivanos and Entso Veliou and Theodora Anastasiou and Jelle Keizer and Blaž Fortuna and Dunja Mladenić},
journal= {arXiv preprint arXiv:2209.13963},
year = {2022}
}