Abstract
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While this can be addressed as a supervised learning problem, a significantly more challenging problem is that of detecting the unknown/unseen anomaly case that takes us instead into the space of a one-class, semi-supervised learning paradigm. We introduce such a novel anomaly detection model, by using a conditional generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space. Employing encoder-decoder-encoder sub-networks in the generator network enables the model to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image. The use of the additional encoder network maps this generated image to its latent representation. Minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution—an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches.
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Notes
- 1.
The code is available on https://github.com/samet-akcay/ganomaly.
References
OSCT Borders X-ray Image Library: UK Home Office Centre for Applied Science and Technology (CAST). Publication Number: 146/16 (2016)
Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016). https://doi.org/10.1016/J.JNCA.2016.04.007. https://www.sciencedirect.com/science/article/pii/S1084804516300571
Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278–288 (2016). https://doi.org/10.1016/J.FUTURE.2015.01.001. https://www.sciencedirect.com/science/article/pii/S0167739X15000023
Ahmed, M., Naser Mahmood, A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016). https://doi.org/10.1016/J.JNCA.2015.11.016. https://www.sciencedirect.com/science/article/pii/S1084804515002891
Akcay, S., Kundegorski, M.E., Willcocks, C.G., Breckon, T.P.: Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery. IEEE Trans. Inf. Forensics Secur. 13(9), 2203–2215 (2018). https://doi.org/10.1109/TIFS.2018.2812196
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2, 1–18 (2015)
Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. In: 2017 ICLR, April 2017. http://arxiv.org/abs/1701.04862
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 214–223, Sydney, Australia, 06–11 August 2017. http://proceedings.mlr.press/v70/arjovsky17a.html
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection. ACM Comput. Surv. 41(3), 1–58 (2009). https://doi.org/10.1145/1541880.1541882
Chen, X., et al.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)
Creswell, A., Bharath, A.A.: Inverting the generator of a generative adversarial network (II). arXiv preprint arXiv:1802.05701 (2018)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)
Dimokranitou, A.: Adversarial autoencoders for anomalous event detection in images. Ph.D. thesis, Purdue University (2017)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: International Conference on Learning Representations (ICLR), Toulon, France, April 2017. http://arxiv.org/abs/1605.09782
Dumoulin, V., et al.: Adversarially learned inference. In: ICLR (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742 (2016)
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004). https://doi.org/10.1023/B:AIRE.0000045502.10941.a9
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448–456, Lille, France, 07–09 July 2015. http://proceedings.mlr.press/v37/ioffe15.html
Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976, July 2017. https://doi.org/10.1109/CVPR.2017.632
Kinga, D., Adam, J.B.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), vol. 5 (2015)
Kiran, B.R., Thomas, D.M., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/
Lipton, Z.C., Tripathi, S.: Precise recovery of latent vectors from generative adversarial networks. In: ICLR Workshop (2017)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. In: ICLR (2016)
Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Process. 83(12), 2481–2497 (2003). https://doi.org/10.1016/J.SIGPRO.2003.07.018. https://www.sciencedirect.com/science/article/pii/S0165168403002020
Markou, M., Singh, S.: Novelty detection: a review-part 2: neural network based approaches. Signal Process. 83(12), 2499–2521 (2003). https://doi.org/10.1016/J.SIGPRO.2003.07.019. https://www.sciencedirect.com/science/article/pii/S0165168403002032
Medel, J.R., Savakis, A.: Anomaly detection in video using predictive convolutional long short-term memory networks. CoRR abs/1612.0 (2016)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)
Ravanbakhsh, M., Sangineto, E., Nabi, M., Sebe, N.: Training adversarial discriminators for cross-channel abnormal event detection in crowds. CoRR abs/1706.0 (2017). http://arxiv.org/abs/1706.07680
Rogers, T.W., Jaccard, N., Morton, E.J., Griffin, L.D.: Automated X-ray image analysis for cargo security: critical review and future promise. J. X-Ray Sci. Technol. (Prepr.) 25, 1–24 (2016)
Sabokrou, M., Fathy, M., Hoseini, M., Klette, R.: Real-time anomaly detection and localization in crowded scenes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 56–62 (2015). https://doi.org/10.1109/CVPRW.2015.7301284, http://ieeexplore.ieee.org/document/7301284/
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)
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Akcay, S., Atapour-Abarghouei, A., Breckon, T.P. (2019). GANomaly: Semi-supervised Anomaly Detection via Adversarial Training. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_39
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