A Deep Learning approach for the Motion Picture Content Rating

The film industry brings thousands of films to life every year. Not all of them are suitable for everyone, especially those with violent content. A content rating system is designed for evaluating the content and reporting the suitability for children, teenagers, or adults. It assists content providers during the assignment of rating levels for movies and, on the other hand, it can be useful for users to block violent content directly on their devices. However, applying for content ratings for movies can be tedious, prone to personal judgment, and also impossible if we also consider the videos on video-sharing websites. This work provides a motion picture content rating model to automatically classify and censor violent scenes using a Deep Learning (DL) approach. We collect a large amount of data searching for visual elements, such as blood or weapons, and manually label them according to a rating scale. Then we employ the Convolutional Neural Network (CNN) Inception v3 for training and validating. The CNN is modified, and additional regularization techniques are adopted to avoid overfitting during the training step. Finally, we design a video post-processing algorithm to refine the network output. Preliminary results demonstrate the effectiveness of our automatic classifier for supporting content providers to assign the rating and encourage further investigations on the use of DL.