2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-21.6
Paper Title Sequential Adversarial Anomaly Detection with Deep Fourier Kernel
Authors Shixiang Zhu, Henry Yuchi, Minghe Zhang, Yao Xie, Georgia Institute of Technology, United States
SessionMLSP-21: Generative Neural Networks
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SLER] Sequential learning; sequential decision methods
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We present a novel adversarial detector for the anomalous sequence when there are only one-class training samples. The detector is developed by finding the best detector that can discriminate against the worst-case, which statistically mimics the training sequences. We explicitly capture the dependence in sequential events using the marked point process with a deep Fourier kernel. The detector evaluates a test sequence and compares it with an optimal time-varying threshold, which is also learned from data. Using numerical experiments on simulations and real-world datasets, we demonstrate the superior performance of our proposed method.