A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes. de Freitas, P. V. A.; Mendes, P. R. C.; Santos, G. N. P. d.; Busson, A. J. G.; Guedes, Á. L.; Colcher, S.; and Milidiú, R. L.
A Multimodal CNN-based Tool to Censure Inappropriate Video Scenes [link]Paper  abstract   bibtex   
Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal\textasciitilde(using audio and image features) architecture based on Convolutional Neural Networks (CNNs) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95\textbackslash% and 98.94\textbackslash% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.
@article{de_freitas_multimodal_2019,
  title = {A Multimodal {CNN}-based Tool to Censure Inappropriate Video Scenes},
  url = {http://arxiv.org/abs/1911.03974},
  abstract = {Due to the extensive use of video-sharing platforms and services for their storage, the amount of such media on the internet has become massive. This volume of data makes it difficult to control the kind of content that may be present in such video files. One of the main concerns regarding the video content is if it has an inappropriate subject matter, such as nudity, violence, or other potentially disturbing content. More than telling if a video is either appropriate or inappropriate, it is also important to identify which parts of it contain such content, for preserving parts that would be discarded in a simple broad analysis. In this work, we present a multimodal{\textasciitilde}(using audio and image features) architecture based on Convolutional Neural Networks ({CNNs}) for detecting inappropriate scenes in video files. In the task of classifying video files, our model achieved 98.95{\textbackslash}\% and 98.94{\textbackslash}\% of F1-score for the appropriate and inappropriate classes, respectively. We also present a censoring tool that automatically censors inappropriate segments of a video file.},
  journaltitle = {{arXiv}:1911.03974 [cs]},
  author = {de Freitas, Pedro V. A. and Mendes, Paulo R. C. and Santos, Gabriel N. P. dos and Busson, Antonio José G. and Guedes, Álan Livio and Colcher, Sérgio and Milidiú, Ruy Luiz},
  date = {2019-11-10},
}
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