  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~(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\% and 98.94\% 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.
