Seamless Nudity Censorship: an Image-to-Image Translation Approach based on Adversarial Training
Martin D. Móre, Douglas M. Souza, Jônatas Wehrmann, Rodrigo Coelho Barros · 2018
The easy access and widespread of the Internet makes it easier than ever to reach content of any kind at any moment, and while that poses several advantages, there is also the fact that sensitive audiences may be inadvertently exposed to nudity content they did not ask for. Virtually every work on nudity and pornography censorship focus solely on performing binary classification, where the result is used to decide whether to completely ignore the accessed content or not. Such an approach may compromise user experience because the entire content, either images or frames of a video, has to be removed/blocked. In this paper, we propose a paradigmatic shift in the literature of adult censorship: instead of detecting and excluding the identified content, we propose to automatically filter out only the sensitive regions of an image. For that, we have developed an image-to-image translation approach based on adversarial training that implicitly locates sensitive regions in images and covers them whilst preserving its semantics, i.e., putting appropriate clothing. We test this novel approach on images of nude women, in which we are capable of automatically generating bikinis that cover the sensitive parts without the additional effort of previously annotating body parts. Our results are visually impressive, proving that it is possible to perform seamless nudity censorship with small effort of data collection and annotation.