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Perceptual Image Anomaly Detection

  • Nina TuluptcevaEmail author
  • Bart Bakker
  • Irina Fedulova
  • Anton Konushin
Conference paper
  • 142 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)

Abstract

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of “normal” data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predefined latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance.

Keywords

Anomaly detection Out-of-distribution detection Deep learning Generative Adversarial Networks 

References

  1. 1.
    Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481–490 (2019)Google Scholar
  2. 2.
    Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)CrossRefGoogle Scholar
  3. 3.
    An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE 2015-2, pp. 1–18 (2015)Google Scholar
  4. 4.
    Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)
  5. 5.
    Chen, Y., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: ICIP, vol. 1, pp. 34–37. Citeseer (2001)Google Scholar
  6. 6.
    Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Image anomaly detection with generative adversarial networks. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 3–17. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-10925-7_1CrossRefGoogle Scholar
  7. 7.
    Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 262–270 (2015)Google Scholar
  8. 8.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46475-6_43CrossRefGoogle Scholar
  11. 11.
    Kiran, B., Thomas, D., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)CrossRefGoogle Scholar
  12. 12.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)Google Scholar
  13. 13.
    Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Cluster Comput. 22, 949–961 (2019) CrossRefGoogle Scholar
  14. 14.
    LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database 2, 18 (2010). AT&T Labs. http://yann.lecun.com/exdb/mnist
  15. 15.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  16. 16.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision, December 2015Google Scholar
  17. 17.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library: COIL-100. Technical report, CUCS-006-96 (1996)Google Scholar
  18. 18.
    Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065–1076 (1962)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Perera, P., Nallapati, R., Xiang, B.: OCGAN: one-class novelty detection using GANs with constrained latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2898–2906 (2019)Google Scholar
  20. 20.
    Pidhorskyi, S., Almohsen, R., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. In: Advances in Neural Information Processing Systems, pp. 6822–6833 (2018)Google Scholar
  21. 21.
    Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4390–4399 (2018)Google Scholar
  22. 22.
    Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)Google Scholar
  24. 24.
    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_12CrossRefGoogle Scholar
  25. 25.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)Google Scholar
  26. 26.
    Spigler, G.: Denoising autoencoders for overgeneralization in neural networks. arXiv preprint arXiv:1709.04762 (2017)
  27. 27.
    Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
  28. 28.
    Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3485–3492. IEEE (2010)Google Scholar
  29. 29.
    Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)
  30. 30.
    Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727–736. IEEE (2018)Google Scholar
  31. 31.
    Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665–674. ACM (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Philips ResearchMoscowRussia
  2. 2.Philips ResearchEindhovenThe Netherlands
  3. 3.Lomonosov Moscow State UniversityMoscowRussia

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