Video pornography detection through deep learning techniques and motion information

In this paper, the authors deal with a growing issue of our connected society: automated sensitive media (pornographic, violent, gory, etc.) filtering. A range of applications has increased societal interest on the problem, e.g., detecting inappropriate behavior via surveillance cameras; or curtailing the exchange of sexually-charged instant messages, also known as “sexting”, by minors. In addition, law enforcers may use pornography filters as a first sieve when looking for child pornography in the forensic examination of computers, or Internet content. The main application, however, remains preventing uploading or accessing undesired content for certain demographics (e.g., minors), or environments (e.g., schools, workplace).

In spite of the success of deep learning techniques in the computer vision arena, their literature on pornography detection is very scarce. In this work, the authors design and develop deep learning-based approaches to automatically extracting discriminative spatio-temporal characteristics for filtering pornographic content in videos. The evaluation of the proposed techniques shows that the association of Deep Learning with the combined use of static and motion information considerably improves pornography detection. Not only over current scientific state of the art, but also over off-the-shelf software solutions.

The contributions of this paper are three-fold:

i) A novel method for classifying pornographic videos, using convolutional neural networks along with static and motion information;

ii) A new technique for exploring the motion information contained in the MPEG motion vectors;

iii) A study of different forms of combining the static and motion information extracted from questioned videos.

Mauricio Perez, Sandra Avila, Daniel Moreira, Daniel Moraes, Vanessa Testoni, Eduardo Valle, Siome Goldenstein, Anderson Rocha, Video pornography detection through deep learning techniques and motion information, Neurocomputing, Volume 230, 22 March 2017, Pages 279-293, ISSN 0925-2312,

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One Response to Video pornography detection through deep learning techniques and motion information

  1. Pingback: Our research about pornography detection on the News | RECOD — Reasoning for Complex Data

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