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arXiv · 2409.04982

2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures

Xinheng Xie·Kureha Yamaguchi·Margaux Leblanc·Simon Malzard·Varun Chhabra·Victoria Nockles·Yue Wu
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Abstract

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.

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Xinheng Xie, Kureha Yamaguchi, Margaux Leblanc, Simon Malzard, Varun Chhabra, Victoria Nockles, Yue Wu. 2024-09-08. 2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures. https://arxiv.org/abs/2409.04982

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