A study on Anomaly Detection GAN-based methods on image data

• Emanuel Silva FACENS
• Johannes Lochter FACENS

DOI: https://doi.org/10.5753/eniac.2019.9337

Resumo

The anomaly detection task is a well know problem being researched among a variety of areas, including machine learning. The task is to identify data patterns that have a non expected behaviour, that can be a malicious data sent by an attacker or a unexpected valid behaviour, in both cases the anomaly need to be identified. With the advance of deep learning based techniques showing that this class of algorithms can learn high-dimensional and complex data patterns, naturally it became an option to the anomaly detection task. Recent researches in literature are using a sub-field of deep learning algorithms named Generative Adversarial Networks for predicting anomalous samples, since the original method can learn the data distribution. These new techniques make some changes for the anomaly detection task, and this work provides a briefly review on these methods and provides a comparison with well known methods.

Palavras-chave: Applications of Artificial Intelligence, Machine Learning, Artificial Neural Networks, Deep Learning

Referências

Adewumi, A. O. and Akinyelu, A. A. (2017). A survey of machine-learning and natureinspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2):937–953.

Akcay, S., Atapour-Abarghouei, A., and Breckon, T. P. (2018). GANomaly: SemiSupervised Anomaly Detection via Adversarial Training. arXiv e-prints, page arXiv:1805.06725.

Alguliyev, R., Aliguliyev, R., and Sukhostat, L. (2017). Anomaly detection in big data based on clustering. Statistics, Optimization Information Computing, 5.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.

Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn., 30(7):1145–1159.

Brock, A., Donahue, J., and Simonyan, K. (2018). Large Scale GAN Training for High Fidelity Natural Image Synthesis. arXiv e-prints, page arXiv:1809.11096.

Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Comput. Surv., 41(3):15:1–15:58.

Curtó, J. D., Zarza, H. C., and Kim, T. (2017). High-Resolution Deep Convolutional Generative Adversarial Networks. arXiv e-prints, page arXiv:1711.06491.

Donahue, J., Krähenbühl, P., and Darrell, T. (2016). Adversarial Feature Learning. arXiv e-prints, page arXiv:1605.09782.

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv eprints, page arXiv:1406.2661.

Hodge, V. and Austin, J. (2004). A survey of outlier detection methodologies. Artif. Intell. Rev., 22(2):85–126.

Jan Latecki, L., Lazarevic, A., and Pokrajac, D. (2007). Outlier detection with kernel density functions. pages 61–75.

Kiran, B. R., Thomas, D. M., and Parakkal, R. (2018). An overview of deep learning based methods for pervised and semi-supervised anomaly detection in videos. arXiv e-prints, page arXiv:1801.03149.

Kwon, D., Kim, H., Kim, J., C. Suh, S., Kim, I., and Kim, K. (2017). A survey of deep learning-based network anomaly detection. Cluster Computing.

Perera, P., Nallapati, R., and Xiang, B. (2019). OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations. arXiv e-prints, page arXiv:1903.08550.

Phuc Ngo, C., Aristo Winarto, A., Kou Khor Li, C., Park, S., Akram, F., and Lee, H. K. (2019). Fence GAN: Towards Better Anomaly Detection. arXiv e-prints, page arXiv:1904.01209.

Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. page arXiv:1511.06434.

Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G. (2017). Usupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. page arXiv:1703.05921.

Shetty, R., Fritz, M., and Schiele, B. (2018). Adversarial Scene Editing: Automatic Object Removal from Weak Supervision. arXiv e-prints, page arXiv:1806.01911.

Silva, T. (2018). An intuitive introduction to generative adversarial networks (gans).

Zenati, H., Romain, M., Foo, C. S., Lecouat, B., and Ramaseshan Chandrasekhar, V. (2018). Adversarially Learned Anomaly Detection. arXiv e-prints, page arXiv:1812.02288.

Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., and Chawla, N. V. (2018). A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. arXiv e-prints, page arXiv:1811.08055.