Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores.

Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. ... Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. ... Detecting anomalies in time series using GAN requires modelling the normal behaviour of time series data using the adversarial training process and then detecting anomalies using an anomaly score that ...

arXiv:2008.09567v2 fatcat:72slzqjxebamrfdffto4vxxb74

Multiple Versions

Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. ... Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. ... Detecting anomalies in time series using GAN requires modelling the normal behaviour of time series data using the adversarial training process and then detecting anomalies using an anomaly score that ...

doi:10.1109/ssci47803.2020.9308512 fatcat:pvaun22ijfehfbqzfvmgl7xdou

The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies. ... We show that such a representation combined with the networks reconstruction error can be utilized as an unsupervised and highly adaptive anomaly assessment. ... ACKNOWLEDGMENTS We thank the members of the statistics department at Deutsche Bundesbank and PwC Europe's Forensic Services for their valuable review and remarks. ...

arXiv:1908.00734v1 fatcat:iew3huhrprfyzmdfiurkobj5zm

Then, two anomaly scores for 1DAAE were proposed to detect fault samples, one based on reconstruction errors, and the other based on latent variable distribution. ... convolution layers for the encoder to obtain better features and the adversarial thought to impose the latent variable to cluster into a prior distribution. ... The 1DAAE model is composed of encoder 𝑓 and decoder 𝑔, which could be used to formulate the anomaly scores to detect fault samples. ...

doi:10.3390/pr11020384 fatcat:ylthjks565dp3jlq32o44raodi

DOAJ

In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions ... We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. ... AAEs for anomaly detection were first proposed in (Leveau & Joly, 2017) , using a Gaussian mixture model as prior. ...

arXiv:1901.06355v1 fatcat:2whvuhwlc5bndk5j37mnrqkgsm

Second, we put forward an alternative measure of anomaly score to replace the reconstruction-based metric which has been traditionally used in generative model-based anomaly detection methods. ... The use of AMA produces better feature representations that improve anomaly detection performance. ... Authors thank Ritesh Soni, Steven Loscalzo, Bayan Bruss, Samuel Sharpe and Jason Wittenbach for helpful discussions. ...

arXiv:2003.10713v3 fatcat:rhff735uxrborke4rwisjs6wm4

Multiple Versions

Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. ... We achieve stateof-the-art performance on standard image benchmark datasets and visual inspection of the most anomalous samples reveals that our method does indeed return anomalies. ... In this paper we present a novel deep learning based approach to anomaly detection which uses generative adversarial networks (GANs) (Goodfellow et al., 2014) . ...

doi:10.1007/978-3-030-10925-7_1 fatcat:motkgpr5cvgt7kencg3h2hj3wy

In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. ... Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real-world datasets. ... Adversarial autoencoders (AAE) [19] avoid using the KL divergence by adopting adversarial learning, to learn broader set of distributions as priors for the latent code. ...

arXiv:1804.04876v1 fatcat:xl7nl6tdgjas7dxiso26gsadea

We finally accomplish the detection process with the extracted probabilistic representation data using the strategy of Gaussian mixture model estimation to fully leverage the spatial information. ... Due to the powerful reconstruction ability, deep learning based hyperspectral anomaly detection methods have been prevalent in recent years. ... Different from target detection, anomaly detection is a very challenging and promising task without any prior knowledge [10] . ...

doi:10.1109/jstars.2022.3182538 fatcat:44ywhf7huneqnm2coe3awboaiu

DOAJ

It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. ... In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble and predictive ... It uses a reconstructionbased anomaly score and detects data with a score higher than the state-based threshold as anomalies. ...

arXiv:2202.10001v1 fatcat:mydjcix23nha5eomol652nlmfm

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ... The hyperspectral anomaly detection and generative adversarial networks are reviewed in Section 2. The MPN methodology is concretely described in Section 3. ... Introduction Due to hundreds of continuous bands, hyperspectral imagery (HSI) obtains high spectral resolution and abundant spectral information, which has been widely used in anomaly detection, target ...

doi:10.3390/rs14122859 dblp:journals/remotesensing/ZhongLXLJ22 fatcat:ol3ubd2xsrcp5cq4g2g5c5kqoe

DOAJ Szczepanski

We use the WM-811k dataset, which consists of a real-world wafer map. We compare the F1 score performance of our model with DSVDD and AAE. ... In this paper, we propose a one-class classification method using an Adversarial Autoencoder (AAE) with Deep Support Vector Data Description (DSVDD) prior, which generates random vectors within the hypersphere ... In this paper, we use AAE for one class classification based on a generative model, and DSVDD which is well known for anomaly detection. ...

arXiv:2107.08823v1 fatcat:4g5y5mxbyvbrpec7gonts76ipi

Open Access

Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. ... In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. ... Anomaly detection methods based on traditional probability statistics require statistical assumptions about the model through prior knowledge, and the data not conforming to the prior knowledge assumptions ...

doi:10.1155/2022/8753323 pmid:36337267 pmcid:PMC9629927 fatcat:2xbgm4rr3zavdjcv7dbrdhnt44

DOAJ

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ... In anomaly detection, it uses the error of each test data as an anomaly score, and a data point with a high anomaly score is considered an anomaly. ... The main idea of the plug-and-play prior used is the most advanced prior among existing priors [35, 36] and CNN-based denoisers [33, 37] , rather than creating a new prior. ...

doi:10.3390/rs14091973 dblp:journals/remotesensing/HuXFDZJWHLZCWC22 fatcat:vwb3azo7cjgopaj7lrbwp6wzki

DOAJ Szczepanski

Autoencoders have been extensively used in the development of recent anomaly detection techniques. ... We also provide a theoretical analysis and numerical simulations that help demonstrate the underlying process that unfolds during training and how it helps with anomaly detection. ... To use the trained model for anomaly detection, the overall loss associated with a sample is used as the anomaly score. ...

arXiv:2306.12627v2 fatcat:rodwdvdbdncivonxozxevzzt54

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TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks [article]

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TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

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Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks [article]

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A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE)

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Robust Anomaly Detection in Images using Adversarial Autoencoders [article]

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Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [article]

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Image Anomaly Detection with Generative Adversarial Networks [chapter]

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Group Anomaly Detection using Deep Generative Models [article]

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Normalizing Flow-Based Probability Distribution Representation Detector for Hyperspectral Anomaly Detection

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Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection [article]

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Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery

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One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior [article]

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Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

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Hyperspectral Anomaly Detection Using Deep Learning: A Review

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Targeted collapse regularized autoencoder for anomaly detection: black hole at the center [article]

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