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Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

Akcay, Akcay; Atapour-Abarghouei, Amir; Breckon, Toby P.

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection Thumbnail


Authors

Akcay Akcay



Abstract

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data distribution based on some measure of normality. The most significant challenge in real-world anomaly detection problems is that available data is highly imbalanced towards normality (i.e. non-anomalous) and contains at most a sub-set of all possible anomalous samples - hence limiting the use of well-established supervised learning methods. By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain, and hence detect abnormality based on deviation from this model. Our proposed approach employs an encoder-decoder convolutional neural network with skip connections to thoroughly capture the multiscale distribution of the normal data distribution in image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within image space and a lower-dimensional embedding vector space encoding. Minimizing the reconstruction error metric within both the image and hidden vector spaces during training aids the model to learn the distribution of normality as required. Higher reconstruction metrics during subsequent test and deployment are thus indicative of a deviation from this normal distribution, hence indicative of an anomaly. Experimentation over established anomaly detection benchmarks and challenging real-world datasets, within the context of X-ray security screening, shows the unique promise of such a proposed approach.

Citation

Akcay, A., Atapour-Abarghouei, A., & Breckon, T. P. (2019). Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2019.8851808

Conference Name Proc. Int. Joint Conference on Neural Networks
Conference Location Budapest, Hungary
Acceptance Date Mar 7, 2019
Online Publication Date Sep 30, 2019
Publication Date Sep 30, 2019
Deposit Date Apr 2, 2019
Publicly Available Date Mar 29, 2024
Publisher Institute of Electrical and Electronics Engineers
Book Title Proceedings of the International Joint Conference on Neural Networks
DOI https://doi.org/10.1109/ijcnn.2019.8851808
Related Public URLs http://atapour.co.uk/papers/akcay19skip.pdf

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