InFeRno: an intelligent framework for recognizing pornographic web pages

Authors:
Sotiris Karavarsamis;Nikos Ntarmos;Konstantinos Blekas
Affiliations:
Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Year:
2011

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Abstract

In this work we present InFeRno, an intelligent web pornography elimination system, classifying web pages based solely on their visual content. The main characteristics of our system include: (i) a powerful vector space with a small but sufficient number of features that manage to improve the discriminative ability of the SVM classifier; (ii) an extra class (bikini) that strengthens the performance of the classifier; (iii) an overall classification scheme that achieves high accuracy at considerably lower runtime costs compared to current state-of-the-art systems; and (iv) a full-fledged implementation of the proposed system capable of being integrated with ICAP-aware web proxy cache servers.