Adult content detection in videos with convolutional and recurrent neural networks

Abstract

The amount of adult content on the Internet grows daily. Much of the pornographic content is unconstrained and freely-available for all users, requiring parents to make use of parental control strategies for protecting their children. Current parental control devices depend on human intervention, and hence there is the need of computational approaches for automatically detecting and blocking pornographic content. Toward that goal, this paper proposes ACORDE, a novel deep learning architecture that comprises both convolutional neural networks and LSTM recurrent networks for adult content detection in videos. Experiments over the freely-available NPDI dataset show that ACORDE significantly outperforms the previous state-of-the-art approaches for this task, decreasing by half the number of false positives and by a third the number of false negatives.