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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Zhelonkin, D., Karpov, N. (2020). Training Effective Model for Real-Time Detection of NSFW Photos and Drawings. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_31
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DOI: https://doi.org/10.1007/978-3-030-39575-9_31
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