1,939 research outputs found
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Anomaly Detection on Graph Time Series
In this paper, we use variational recurrent neural network to investigate the
anomaly detection problem on graph time series. The temporal correlation is
modeled by the combination of recurrent neural network (RNN) and variational
inference (VI), while the spatial information is captured by the graph
convolutional network. In order to incorporate external factors, we use feature
extractor to augment the transition of latent variables, which can learn the
influence of external factors. With the target function as accumulative ELBO,
it is easy to extend this model to on-line method. The experimental study on
traffic flow data shows the detection capability of the proposed method
Recent Advances in Anomaly Detection Methods Applied to Aviation
International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
Mouse dynamics is a potential means of authenticating users. Typically, the
authentication process is based on classical machine learning techniques, but
recently, deep learning techniques have been introduced for this purpose.
Although prior research has demonstrated how machine learning and deep learning
algorithms can be bypassed by carefully crafted adversarial samples, there has
been very little research performed on the topic of behavioural biometrics in
the adversarial domain. In an attempt to address this gap, we built a set of
attacks, which are applications of several generative approaches, to construct
adversarial mouse trajectories that bypass authentication models. These
generated mouse sequences will serve as the adversarial samples in the context
of our experiments. We also present an analysis of the attack approaches we
explored, explaining their limitations. In contrast to previous work, we
consider the attacks in a more realistic and challenging setting in which an
attacker has access to recorded user data but does not have access to the
authentication model or its outputs. We explore three different attack
strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we
show that they are able to evade the functionality of the authentication
models, thereby impacting their robustness adversely. We show that
imitation-based attacks often perform better than surrogate-based attacks,
unless, however, the attacker can guess the architecture of the authentication
model. In such cases, we propose a potential detection mechanism against
surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks
(IJCNN). Update of DO
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