Please use this identifier to cite or link to this item:
Type: Artigo
Title: Video Pornography Detection Through Deep Learning Techniques And Motion Information
Author: Perez
Mauricio; Avila
Sandra; Moreira
Daniel; Moraes
Daniel; Testoni
Vanessa; Valle
Eduardo; Goldenstein
Siome; Rocha
Abstract: Recent literature has explored automated pornographic detection a bold move to replace humans in the tedious task of moderating online content. Unfortunately, on scenes with high skin exposure, such as people sunbathing and wrestling, the state of the art can have many false alarms. This paper is based on the premise that incorporating motion information in the models can alleviate the problem of mapping skin exposure to pornographic content, and advances the bar on automated pornography detection with the use of motion information and deep learning architectures. Deep Learning, especially in the form of Convolutional Neural Networks, have striking results on computer vision, but their potential for pornography detection is yet to be fully explored through the use of motion information. We propose novel ways for combining static (picture) and dynamic (motion) information using optical flow and MPEG motion vectors. We show that both methods provide equivalent accuracies, but that MPEG motion vectors allow a more efficient implementation. The best proposed method yields a classification accuracy of 97.9% an error reduction of 64.4% when compared to the state of the art on a dataset of 800 challenging test cases. Finally, we present and discuss results on a larger, and more challenging, dataset.
Subject: Pornography Classification
Deep Learning And Motion Information
Optical Flow
Mpeg Motion Vectors
Sensitive Video Classification
Editor: Elsevier Science BV
Citation: Neurocomputing. Elsevier Science Bv, v. 230, p. 279 - 293, 2017.
Rights: fechado
Identifier DOI: 10.1016/j.neucom.2016.12.017
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000394061800025.pdf1.1 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.