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

De novo design of small molecules using variational and conditional variational autoencoders

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Jordi Vitrià i Marca[en] Chemical space is estimated to contain over 10 60 small synthetically feasible molecules and so far only a fraction of the space has been explored. Experimental techniques are time-consuming and expensive so computational methods, such as machine learning, are needed for efficient exploration. Here we looked at generative models, more specifically variational autoencoder (VAE) and conditional variational autoencoder (CVAE), used for designing new molecules. In the first part, we evaluated already written VAE and in the second part, we upgraded it to the CVAE. For the conditional vectors in CVAE we used B4 Signatures generated from Chemical Checker describing molecular properties. Both models performed well, however, CVAE showed many advantages