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

arXiv: https://arxiv.org/abs/1903.01031 · HTML: https://mlsanity.com/paper/1903.01031/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [Federated Learning-based Active Authentication on Mobile Devices](https://mlsanity.com/paper/2104.07158/) (2021)
- [Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection](https://mlsanity.com/paper/2109.14020/) (2021)
- [Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation](https://mlsanity.com/paper/2103.16851/) (2021)
- [A Joint Representation Learning and Feature Modeling Approach for One-class Recognition](https://mlsanity.com/paper/2101.09782/) (2021)

## Abstract (arXiv)

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.