We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection and mobile active authentication datasets show that the proposed Deep One-Class (DOC) classification method achieves significant improvements over the state-of-the-art.
In @cite_16 , visual anomalies in wire ropes are detected based on Gaussian process modeling. Anomaly detection is performed by maximizing KL divergence in @cite_35 , where the underlying distribution is assumed to be a known Gaussian. A detailed description of various anomaly detection methods can be found in @cite_3 .
@cite_35 @cite_16 @cite_3
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Automatic visual inspection of wire ropes is an important but challenging task, as anomalies in the rope are usually unobtrusive. Certainly, a reliable anomaly detection is essential to assure the safety of the ropes. A one-class classification approach for the automatic detection of anomalies in wire ropes is presented. Furthermore, the performance of different well-established features from the field of textural defect detection are compared with respect to this task. The faultless rope structure is thereby modeled by a Gaussian mixture model and outliers are regarded as anomaly. To prove the practical applicability, a careful evaluation of the presented approach is performed on real-life rope data. In doing so, a special interest was put on the robustness of the model with respect to unintentional outliers in the training and on its generalization ability given further data from an identically constructed rope. The results prove good recognition rates accompanied by a high generalization ability and robustness to outliers in the training set. Anomaly Detection in Wire Ropes is an important problem. Detecting suspicious anomalies in the rope surface is challenging because of the variety of its visual appearance caused by reflections or mud on the rope surface. This hinders the discrimination between uncritical variations and small defects within the rope surface enormously. The fact that nearly no defective samples are available to train a supervised system relates this problem to the concept of one-class classification (OCC). In this work we show how to utilize one-class classification with Gaussian processes (GP) to detect anomalies in wire ropes. The method allows modeling the distribution of non-defective data in a non-parametric manner. Furthermore, it is really easy to implement (few lines of code), embedded in a Bayesian framework, and can be used with arbitrary kernel functions. Therefore, it is suitable for a wide range of defect localization applications. Our experiments, performed on two real ropes, demonstrate that the GP framework for OCC clearly outperforms former approaches for anomaly detection in wire ropes. The obtained results are comparable with or even outperform those obtained with the Support Vector Data Description, which is the state of the art reference in the field of one-class classification. This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topological deformations and can handle the noise and data unbalance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80 detection rates based on three challenging datasets. As the complexity of cyber-physical systems increases, so does the number of ways an adversary can disrupt them. This necessitates automated anomaly detection methods to detect possible threats. In this paper, we extend our recent results in the field of inference via formal methods to develop an unsupervised learning algorithm. Our procedure constructs from data a signal temporal logic (STL) formula that describes normal system behavior. Trajectories that do not satisfy the learned formula are flagged as anomalous. STL can be used to formulate properties such as “If the train brakes within 500 m of the platform at a speed of 50 km hr, then it will stop in at least 30 s and at most 50 s.” STL gives a more human-readable representation of behavior than classifiers represented as surfaces in high-dimensional feature spaces. STL formulae can also be used for early detection via online monitoring and for anomaly mitigation via formal synthesis. We demonstrate the power of our method with a physical model of a train's brake system. To our knowledge, this paper is the first instance of formal methods being applied to anomaly detection.