2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
<|reference_start|>2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures: 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.<|reference_end|>
@article{xie20242dsig-detect:,
    title={2DSig-Detect: a semi-supervised framework for anomaly detection on image
  data using 2D-signatures},
    author={Xinheng Xie, Kureha Yamaguchi, Margaux Leblanc, Simon Malzard, Varun
  Chhabra, Victoria Nockles, Yue Wu},
    journal={arXiv preprint arXiv:2409.04982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04982},
    primaryClass={cs.CV math.PR stat.ML}
}