86 research outputs found
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
GAN-based Hyperspectral Anomaly Detection
In this paper, we propose a generative adversarial network (GAN)-based
hyperspectral anomaly detection algorithm. In the proposed algorithm, we train
a GAN model to generate a synthetic background image which is close to the
original background image as much as possible. By subtracting the synthetic
image from the original one, we are able to remove the background from the
hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli
(RX) anomaly detector (AD) on the spectral difference image. In the
experimental part, we compare our proposed method with the classical RX,
Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly
detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic
and real hyperspectral images. The detection results show that our proposed
algorithm outperforms the other methods in the benchmark.Comment: 5 page
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Deep Industrial Image Anomaly Detection: A Survey
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
Graph Fairing Convolutional Networks for Anomaly Detection
Graph convolution is a fundamental building block for many deep neural
networks on graph-structured data. In this paper, we introduce a simple, yet
very effective graph convolutional network with skip connections for
semi-supervised anomaly detection. The proposed layerwise propagation rule of
our model is theoretically motivated by the concept of implicit fairing in
geometry processing, and comprises a graph convolution module for aggregating
information from immediate node neighbors and a skip connection module for
combining layer-wise neighborhood representations. This propagation rule is
derived from the iterative solution of the implicit fairing equation via the
Jacobi method. In addition to capturing information from distant graph nodes
through skip connections between the network's layers, our approach exploits
both the graph structure and node features for learning discriminative node
representations. These skip connections are integrated by design in our
proposed network architecture. The effectiveness of our model is demonstrated
through extensive experiments on five benchmark datasets, achieving better or
comparable anomaly detection results against strong baseline methods. We also
demonstrate through an ablation study that skip connection helps improve the
model performance
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