Filtering adult image content with topic models

  • Protecting children from exposure to adult content has become a serious problem in the real world. Current statistics show that, for instance, the average age of first Internet exposure to pornography is 11 years, that the largest consumer group of Internet pornography is the age group of 12-to-17-year-olds and that 90% of the 8-to-16-year-olds have viewed porn online. To protect our children, effective algorithms for detecting adult images are needed. In this research we evaluate the use of probabilistic Latent Semantic Analysis (pLSA) for this task. We will show that topic models based on pLSA can detect adult content with a correct positive rate of 92.7%, while only showing off a false positive rate of 1.9%. Even when using grayscale images only, a correct positive rate of 90.8% at a false positive rate of 2% can be achieved.

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Metadaten
Author:Rainer LienhartGND, Rudolf Hauke
URN:urn:nbn:de:bvb:384-opus4-11106
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/1317
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2009-10)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Contributing Corporation:Advanced US Technology Group, Inc.
Release Date:2009/10/21
Tag:topic models; image classification; adult classification; adult
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Multimedia und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht