One-Class Convolutional Neural Network

Oza, Poojan; Patel, Vishal M.

doi:10.1109/lsp.2018.2889273

Cited by 149 publications

(91 citation statements)

References 35 publications

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“…The former is based on CNN for one-class classification problems. Its idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the convolutional network using the cross-entropy loss to learn a good representation and the decision boundary for a given class [57]. CNN has been widely applied to computationally complex classification tasks, such as image defect detection [80] and face verification [56].…”

Section: Other Classifiersmentioning

confidence: 99%

BlinKey

Zhu

Jin

Xiao

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Virtual Reality (VR) has shown promising potentials in many applications, such as e-business, healthcare, and social networking. Rich information regarding user's activities and their online accounts is stored in VR devices. If it is carelessly unattended, then attackers, including insiders, can make use of the stored information to, for example, perform in-app purchases at the legitimate owner's expenses. Current solutions, mostly following schemes designed for general personal devices, have been proved vulnerable to shoulder-surfing attacks due to the sight blocking caused by the headset. Although there have been efforts trying to fill this gap, they either rely on some highly advanced equipment, such as electrodes to read brainwaves, or introduce heavy cognitive load that has users perform a series of cumbersome authentication tasks. Therefore, an authentication method for VR devices that is robust and convenient is in dire need. In this paper, we present the design, implementation, and evaluation of a two-factor user authentication scheme, BlinKey, for VR devices that are equipped with an eye tracker. A user's secret passcode is a set of recorded rhythms when he/she blinks, together with the unique pupil size variation pattern. We call this passcode as a blinkey, which can be jointly characterized by knowledge-based and biometric features. To examine the performances, BlinKey is implemented on an HTC Vive Pro with a Pupil Labs eye tracker. Through extensive experimental evaluations with 52 participants, we show that our scheme can achieve the average EER as low as 4.0% with only 6 training samples. Besides, it is robust against various types of attacks. BlinKey also exhibits satisfactory usability in terms of login attempts, memorability, and impact of user motions. We also carry out questionnaire-based pre-/post-studies. The survey result indicates that BlinKey is well accepted as a user authentication scheme for VR devices.

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Section: Other Classifiersmentioning

confidence: 99%

BlinKey

Zhu

Jin

Xiao

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Assuming that the extracted features are D-dimensional, the features are appended with the pseudo-negative data generated from a Gaussian, N (µ, σ · I), similar to [31]. Following We use a simple one layer fully connected classifier network (C) with sigmoid activation at the end, as shown in Fig.…”

Section: B Classification Networkmentioning

confidence: 99%

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Oza

Patel

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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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.

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“…This approach is extended in [36] by replacing the hyperplane with an hyper-sphere, and then in [32] by introducing deep neural architectures in these models. Later on, [5] transposes the original one-class SVM model completely into a deep neural architecture, and [28] projects a similar neural architectures as a supervised model by generating pseudo-labels for negative samples.…”

Section: Related Workmentioning

confidence: 99%

Deep Unsupervised System Log Monitoring

Nourtel

Cerisara

Cruz‐Lara

2019

Product-Focused Software Process Improvement

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This work proposes a new unsupervised deep generative model for system logs. It is designed to be generic and may be used in various downstream anomaly detection tasks, such as system failure or intrusion detection. It is based on the (reasonable) assumption that most log lines follow rather fixed syntactic structures, which enables us to replace the costly traditional convolutional and recurrent architectures by a much faster component: a deep averaging network. Our model still exploits a standard recurrent model with attention to capture the dependencies between successive log lines. We experimentally validate the proposed generative model on a real dataset obtained from a state-of-the-art High Performance Computing cluster and show the effectiveness of the proposed approach in modeling the "normal" behaviour of the system.

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One-Class Convolutional Neural Network

Cited by 149 publications

References 35 publications

BlinKey

BlinKey

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Deep Unsupervised System Log Monitoring

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