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

• URL: http://arxiv.org/abs/2409.04982v1
• Date: Sun, 8 Sep 2024 05:35:05 GMT
• Title: 2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
• Authors: Xinheng Xie, Kureha Yamaguchi, Margaux Leblanc, Simon Malzard, Varun Chhabra, Victoria Nockles, Yue Wu,
• Abstract summary: This paper introduces a novel technique for anomaly detection in images called 2DSig-Detect. We show both superior performance and a reduction in the time to detect the presence of adversarial perturbations in images.
• Score: 2.6642754249961103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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