Quickest intrusion detection in mobile active user authentication

Perera, Pramuditha; Patel, Vishal M.

doi:10.1109/btas.2016.7791197

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…One-class classification has many applications such as anomaly or abnormality detection [1], [2], [3], [4], [5], novelty detection [6], [7], [8], and user authentication [9], [10], [11], [12], [13], [14]. For example, in novelty detection, it is normally assumed that one does not have a priori knowledge of the novel class data.…”

Section: Introductionmentioning

confidence: 99%

One-Class Convolutional Neural Network

Oza

Patel

2019

IEEE Signal Process. Lett.

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157

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We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the crossentropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. The proposed One Class CNN (OC-CNN) is evaluated on the UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are related to a variety of one class application problems such as user authentication, abnormality detection and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at : github.com/otkupjnoz/oc-cnn.

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