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2020 | OriginalPaper | Buchkapitel

Training Effective Model for Real-Time Detection of NSFW Photos and Drawings

verfasst von : Dmirty Zhelonkin, Nikolay Karpov

Erschienen in: Analysis of Images, Social Networks and Texts

Verlag: Springer International Publishing

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Abstract

Convolutional Neural Networks (CNN) show state of the art results on variety of tasks. The paper presents the scheme how to prepare highly accurate (97% on the test set) and fast CNN for detection not suitable or safe for work (NSFW) images. The present research focuses on investigating questions concerning identifying NSFW pictures with nudity by neural networks. One of the main features of the present work is considering the NSFW class of images not only in terms of natural human nudity but also include cartoons and other drawn pictures containing obscene images of the primary sexual characteristics. Another important considered issue is collecting representative dataset for the problem. The research includes the review of existing nudity detection methods, which are provided by traditional machine learning techniques and quite new neural networks based approaches. In addition, several important problems in NSFW pictures filtering are considered in the study.

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https://github.com/yahoo/open_nsfw.

https://github.com/ryanjay0/miles-deep.

https://github.com/GodelBose/NSFW_Detection.

https://github.com/EBazarov/nsfw_data_source_urls.

https://fapme.me.

Fleck, M.M., Forsyth, D.A., Bregler, C.: Finding naked people. In: Buxton, B., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 593–602. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61123-1_173CrossRef

Short, M.B., Black, L., Smith, A.H., Wetterneck, C.T., Wells, D.E.: A review of Internet pornography use research: methodology and content from the past 10 years. Cyberpsychol. Behav. Soc. Netw. 15(1), 13–23 (2012)CrossRef

Flores, P.I.T., Guillén, L.E.C., Prieto, O.A.N.: Approach of RSOR algorithm using HSV color model for nude detection in digital images. Comput. Inf. Sci. 4(4), 29 (2011)

Platzer, C., Stuetz, M., Lindorfer, M.: Skin sheriff: a machine learning solution for detecting explicit images. In: Proceedings of the 2nd International Workshop on Security and Forensics in Communication Systems, pp. 45–56. ACM (2014)

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRef

Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32CrossRef

Deselaers, T., Pimenidis, L., Ney, H.: Bag-of-visual-words models for adult image classification and filtering. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)

Lopes, A.P.B., Avila, S.E.F.d., Peixoto, A.N.A., Oliveira, R.S., Araújo, A.d.A.: A bag-of-features approach based on hue-sift descriptor for nude detection. In: Proceedings of the XVII European Signal Processing Conference (EUSIPCO), Glasgow, Scotland (2009)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

10.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)

11.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

12.

Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

13.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

14.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015). http://arxiv.org/abs/1512.00567

15.

Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint pp. 1610–02357 (2017)

16.

Moustafa, M.: Applying deep learning to classify pornographic images and videos. arXiv preprint arXiv:1511.08899 (2015)

17.

Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

18.

Avila, S., Thome, N., Cord, M., Valle, E., Araújo, A.D.A.: Pooling in image representation: the visual codeword point of view. Comput. Vis. Image Underst. 117(5), 453–465 (2013)CrossRef

19.

Perez, M., et al.: Video pornography detection through deep learning techniques and motion information. Neurocomputing 230, 279–293 (2017)CrossRef

20.

Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)CrossRef

21.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520. IEEE (2018)

Titel: Training Effective Model for Real-Time Detection of NSFW Photos and Drawings
verfasst von: Dmirty Zhelonkin
Nikolay Karpov
Verlag: Springer International Publishing
Buch: Analysis of Images, Social Networks and Texts
Print ISBN: 978-3-030-39574-2

Electronic ISBN: 978-3-030-39575-9

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-39575-9_31

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