Abstract
With the emergence of the INTERNET and the growth of social networks, the sharing of content, such as images, audios and videos, and access to this content through websites and social networks, has become much greater. Shared content, consisting of images, audio and/or videos, may not be appropriate for all audiences or environments, for various reasons. One of them is in relation to nudity and pornography, which is very present on the INTERNET and social networks, and can cause negative impacts when accessed in business environments, as well as it can cause problems in the development and behavior of children and adolescents. In order to control access to these types of content, it is necessary to develop resources that perform filtering. Therefore, this work seeks to contribute to the development of a tool capable of detecting nudity in images by combining existing image processing techniques, such as the detection of skin color pixels, counting of related elements, zoning techniques and nudity classifiers using machine learning algorithms. Tests carried out on showed an accuracy of 90.5% in the best case.
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