The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models performing image-related tasks, e.g. detection, and classification, are vulnerable to adversarial attacks that can degrade their performance and produce undesirable outcomes. This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect, which uses a 2D-signature-embedded semi-supervised framework rooted in rough path theory. We demonstrate our method in adversarial settings for training-time and test-time attacks, and benchmark our framework against other state of the art methods. Using 2DSig-Detect for anomaly detection, we show both superior performance and a reduction in the computation time to detect the presence of adversarial perturbations in images.
@article{arxiv.2409.04982,
title = {2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures},
author = {Xinheng Xie and Kureha Yamaguchi and Margaux Leblanc and Simon Malzard and Varun Chhabra and Victoria Nockles and Yue Wu},
journal= {arXiv preprint arXiv:2409.04982},
year = {2025}
}